ORCID Profile
0000-0002-0996-3386
Current Organisation
Australian Bureau of Meteorology
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Publisher: Elsevier BV
Date: 1989
Publisher: American Meteorological Society
Date: 2010
Publisher: American Meteorological Society
Date: 03-2014
Abstract: The Australian Community Climate and Earth System Simulator (ACCESS) has been adapted for operational and research applications on tropical cyclones. The base system runs at a resolution of 0.11° and 50 levels. The domain is relocatable and nested in coarser-resolution ACCESS forecasts. Initialization consists of five cycles of four-dimensional variational data assimilation (4DVAR) over 24 h. Forecasts to 72 h are made. Without vortex specification, initial conditions usually contain a weak and misplaced circulation pattern. Significant effort has been devoted to building physically based, synthetic inner-core structures, validated using historical dropsonde data and surface analyses from the Atlantic. Based on estimates of central pressure and storm size, vortex specification is used to filter the analyzed circulation from the original analysis, construct an inner core of the storm, locate it to the observed position, and merge it with the large-scale analysis at outer radii. Using all available conventional observations and only synthetic surface pressure observations from the idealized vortex to correct the initial location and structure of the storm, the 4DVAR builds a balanced, intense 3D vortex with maximum wind at the radius of maximum wind and with a well-developed secondary circulation. Mean track and intensity errors for Australian region and northwest Pacific storms have been encouraging, as are recent real-time results from the Australian National Meteorological and Oceanographic Centre. The system became fully operational in November 2011. From preliminary diagnostics, some interesting structure change features are illustrated. Current limitations, future enhancements, and research applications are also discussed.
Publisher: American Meteorological Society
Date: 06-1993
Publisher: American Meteorological Society
Date: 09-1993
Publisher: Bureau of Meteorology, Australia
Date: 06-2013
DOI: 10.22499/2.6302.001
Publisher: American Meteorological Society
Date: 02-2012
Abstract: An operational surface analysis system for the continent of Australia is presented. The system is specifically designed to mitigate problems that arise when analyzing surface data with a highly inhomogeneous distribution. Hourly analyses of atmospheric pressure at mean sea level, potential temperature, 2-m dewpoint temperature, and 10-m wind components are generated on a ~4-km grid. The system employs a statistical interpolation technique using observations of pressure, temperature, dewpoint, and wind data. The problem of data gaps in space and time is addressed by introducing pseudo-observations. For stations missing a report at analysis time, estimates are reconstructed by interpolating off-time reports. Underobserved areas in the network are identified from precalculated, gridded observation densities for each analysis time, which also yield weights to combine preliminary analysis and first-guess data into pseudo-observations. A regression-based pressure reduction technique, consistent with local reductions at observing sites and devised specifically for this system, is used for accurate and fast conversion of pressure and, indirectly, temperature variables within the system. Analysis accuracy is verified by withholding observations for specific periods. Analyzed fields are shown to be significantly more accurate than the current operational numerical model fields used as a first guess for the high-resolution surface analysis. The system design and analysis accuracies are also assessed within this context and compared with similar overseas developments.
Publisher: Elsevier BV
Date: 04-2009
Publisher: Wiley
Date: 07-2022
DOI: 10.1002/MET.2087
Abstract: High‐resolution regional reanalysis datasets have the potential to provide valuable guidance to emergency management agencies, highlighting areas at risk of severe weather, including estimates of return periods of various hazardous weather phenomena. The BARRA regional reanalysis for Australia comprises a reanalysis for a broad region around Australia at moderately high spatial and temporal resolution (12 km/hourly), together with four subdomains at high resolution (1.5 km/1 h). Here, we document four applications of BARRA developed for emergency management: optimal placement of portable automatic weather stations for fire weather monitoring climatology of low‐level wind shear conducive to cool‐season tornadogenesis development of rainfall intensity–frequency–duration curves based on the gridded reanalysis data and development of a climatology across Australia of parameters associated with severe thunderstorm occurrence.
Publisher: Copernicus GmbH
Date: 24-03-2015
Abstract: Abstract. The atmospheric Unified Model (UM) developed at the UK Met Office is used for weather and climate prediction by forecast teams at a number of international meteorological centres and research institutes on a wide variety of hardware and software environments. Over its 25 year history the UM sources have been optimised for better application performance on a number of High Performance Computing (HPC) systems including NEC SX vector architecture systems and recently the IBM Power6/Power7 platforms. Understanding the influence of the compiler flags, Message Passing Interface (MPI) libraries and run configurations is crucial to achieving the shortest elapsed times for a UM application on any particular HPC system. These aspects are very important for applications that must run within operational time frames. Driving the current study is the HPC industry trend since 1980 for processor arithmetic performance to increase at a faster rate than memory bandwidth. This gap has been growing especially fast for multicore processors in the past 10 years and it can have significant implication for the performance and performance scaling of memory bandwidth intensive applications, such as the UM. Analysis of partially used nodes on Intel Xeon clusters is provided in this paper for short- and medium-range weather forecasting systems using global and limited-area configurations. It is shown that on the Intel Xeon-based clusters the fastest elapsed times and the most efficient system usage can be achieved using partially committed nodes.
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/753031
Abstract: Variational data assimilation (VDA) remains one of the key issues arising in many fields of geosciences including the numerical weather prediction. While the theory of VDA is well established, there are a number of issues with practical implementation that require additional consideration and study. However, the exploration of VDA requires considerable computational resources. For simple enough low-order models, the computational cost is minor and therefore models of this class are used as simple test instruments to emulate more complex systems. In this paper, the sensitivity with respect to variations in the parameters of one of the main components of VDA, the nonlinear forecasting model, is considered. For chaotic atmospheric dynamics, conventional methods of sensitivity analysis provide uninformative results since the envelopes of sensitivity functions grow with time and sensitivity functions themselves demonstrate the oscillating behaviour. The use of sensitivity analysis method, developed on the basis of the theory of shadowing pseudoorbits in dynamical systems, allows us to calculate sensitivity functions correctly. Sensitivity estimates for a simple coupled dynamical system are calculated and presented in the paper. To estimate the influence of model parameter uncertainties on the forecast, the relative error in the energy norm is applied.
Publisher: CSIRO Publishing
Date: 14-02-2022
DOI: 10.1071/ES21013
Abstract: The Australian Bureau of Meteorology’s ‘Australian Parallel Suite’ (APS) operational numerical weather prediction regional Australian Community Climate and Earth-System Simulator (ACCESS) city-based system (APS1 ACCESS-C1) was updated in August 2017 with the commissioning of the APS2 ACCESS-C2. ACCESS-C2 runs over six regional domains. Significant upgrade changes included implementation of Unified Model 8.2 code nesting in the 12 km resolution APS2 ACCESS-R2 regional model and, importantly, an increased horizontal resolution from 4 to 1.5 km, enabling C2 to become the first Australian operational convection-permitting model (CPM). Traditional rainfall verification metrics and Fractions Skill Score show C2 forecast skill over ACCESS-C domains in summer and winter was generally, and in many cases, significantly better than C1. Case studies showed that C2 forecasts had better-detailed wind and precipitation fields, particularly at longer forecast ranges and higher rain rates. The improvements in C2 forecasts were principally due to its CPM ability to simulate high temporal and spatial resolution features, which continue to be of great interest to forecasters. C2 also laid the groundwork for the present day APS3 ACCESS-C forecast C3 and ensemble CE3 models and further development of higher resolution (down to 300 m) fire weather and urban models.
Publisher: American Meteorological Society
Date: 02-2011
Abstract: Previous descriptions of how localized ensemble covariances can be incorporated into variational (VAR) data assimilation (DA) schemes provide few clues as to how this might be done in an efficient way. This article serves to remedy this hiatus in the literature by deriving a computationally efficient algorithm for using nonadaptively localized four-dimensional (4D) or three-dimensional (3D) ensemble covariances in variational DA. The algorithm provides computational advantages whenever (i) the localization function is a separable product of a function of the horizontal coordinate and a function of the vertical coordinate, (ii) and/or the localization length scale is much larger than the model grid spacing, (iii) and/or there are many variable types associated with each grid point, (iv) and/or 4D ensemble covariances are employed.
Publisher: American Meteorological Society
Date: 08-2018
DOI: 10.1175/JTECH-D-17-0183.1
Abstract: A new quality control system, primarily using a naïve Bayesian classifier, has been developed to enable the assimilation of radial velocity observations from Doppler radar. The ultimate assessment of this system is the assimilation of observations in a pseudo-operational numerical weather prediction system during the Sydney 2014 Forecast Demonstration Project. A statistical analysis of the observations assimilated during this period provides an assessment of the data quality. This will influence how observations will be assimilated in the future, and what quality control and errors are applicable. This study compares observation-minus-background statistics for radial velocities from precipitation and insect echoes. The results show that with the applied level of quality control, these echo types have comparable biases. With the latest quality control, the clear air observations of wind are apparently of similar quality to those from precipitation and are therefore suitable for use in high-resolution NWP assimilation systems.
Publisher: Elsevier BV
Date: 12-1989
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 Geophysical Union (AGU)
Date: 2017
DOI: 10.1002/2015WR017738
Publisher: Copernicus GmbH
Date: 24-05-2019
Abstract: Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale ( km) models.
Publisher: American Geophysical Union (AGU)
Date: 12-02-2021
DOI: 10.1029/2020GL090699
Abstract: Aircraft reports are an important source of information for numerical weather prediction (NWP). From March 2020, the COVID‐19 pandemic resulted in a large loss of aircraft data but despite this it is difficult to see any evidence of significant degradation in the forecast skill of global NWP systems. This apparent discrepancy is partly because forecast skill is very variable, showing both day‐to‐day noise and lower frequency dependence on the mean state of the atmosphere. The definitive way to cleanly assess aircraft impact is using a data denial experiment, which shows that the largest impact is in the upper troposphere. The method used by Chen (2020, 0.1029/2020gl088613 ) to estimate the impact of COVID‐19 is oversimplistic. Chen understates the huge importance of satellite data for modern weather forecasts and raises more alarm than necessary about a drop in forecast accuracy.
Publisher: Springer Netherlands
Date: 2016
Publisher: Informa UK Limited
Date: 05-08-2016
Publisher: SPIIRAS
Date: 12-2018
DOI: 10.15622/SP.61.1
Abstract: Mathematical models of the Earth system and its components represent one of the most powerful and effective instruments applied to explore the Earth system's behaviour in the past and present, and to predict its future state considering external influence. These models are critically reliant on a large number of various observations (in situ and remotely sensed) since the prediction accuracy is determined by, amongst other things, the accuracy of the initial state of the system in question, which, in turn, is defined by observational data provided by many different instrument types. The development of an observing network is very costly, hence the estimation of the effectiveness of existing observation network and the design of a prospective one, is very important. The objectives of this paper are (1) to present the adjoint-based approach that allows us to estimate the impact of various observations on the accuracy of prediction of the Earth system and its components, and (2) to illustrate the application of this approach to two coupled low-order chaotic dynamical systems and to the ACCESS (Australian Community Climate and Earth System Simulator) global model used operationally in the Australian Bureau of Meteorology. The results of numerical experiments show that by using the adjoint-based method it is possible to rank the observations by the degree of their importance and also to estimate the influence of target observations on the quality of predictions.
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: Unpublished
Date: 2016
Publisher: Hindawi Limited
Date: 2016
DOI: 10.1155/2016/7943845
Abstract: The Australian Community Climate and Earth-System Simulator (ACCESS) is used to test the sensitivity of heavy precipitation to various model configurations: horizontal resolution, domain size, rain rate assimilation, perturbed physics, and initial condition uncertainties, through a series of convection-permitting simulations of three heavy precipitation (greater than 200 mm day −1 ) cases in different synoptic backgrounds. The larger disparity of intensity histograms and rainfall fluctuation caused by different model configurations from their mean and/or control run indicates that heavier precipitation forecasts have larger uncertainty. A cross-verification exercise is used to quantify the impacts of different model parameters on heavy precipitation. The dispersion of skill scores with control run used as “truth” shows that the impacts of the model resolution and domain size on the quantitative precipitation forecast are not less than those of perturbed physics and initial field uncertainties in these not intentionally selected heavy precipitation cases . The result indicates that model resolution and domain size should be considered as part of probabilistic precipitation forecasts and ensemble prediction system design besides the model initial field uncertainty.
Publisher: MDPI AG
Date: 16-01-2018
DOI: 10.3390/ATMOS9010023
No related grants have been discovered for Peter Steinle.