Journal articles on the topic 'Prediction of Australia'
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1
Cowan, Tim, Matthew Wheeler, and Roger Stone. "Prediction of Northern Australian Rainfall Onset Using the ACCESS-Seasonal Model." Proceedings 36, no. 1 (April 8, 2020): 189. http://dx.doi.org/10.3390/proceedings2019036189.
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The development of the Australian Community Climate Earth-System Simulator-Seasonal version 1 (ACCESS-S1) prediction system signifies a major step in addressing predictive limitations in multi-week to seasonal forecasting. It is anticipated that ACCESS-S1 will provide greater skill in its prediction of the wet season onset and intensity, which are crucial to the viability of cattle grazing across northern Australia. We evaluate the hindcast skill of the ACCESS-S1 for the northern rainfall onset, defined as the date when 50 mm of precipitation has accumulated at a given location from the 1st of September, heralding the start of the seasonal dry-to-wet transition over northern Australia. We show that the raw ACCESS-S1 hindcasts, regridded to a 5 km observed grid, capture the broad-scale features of the median onset, including an early October onset over the western Top End and southeast Queensland. However, the hindcasts fail to capture the later December onsets over central Australia. The greatest improvement in onset skill comes from first calibrating the hindcasts using observations, which outperform the raw model and bias corrected hindcasts over central Australia and the far west in the Pilbara-Gascoyne basin. Based on its simulation of realistic northern rainfall onset dates and variability alone, ACCESS-S1’s prediction performance can be considered an improvement over the older predictive system. As the real-time onset forecasts have were issued using ACCESS-S1 in July 2019, it is expected that the calibrated predictions will help improve the resilience of cattle producers and graziers to drought across northern Australia.
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Lim, Eun-Pa, Harry H. Hendon, David L. T. Anderson, Andrew Charles, and Oscar Alves. "Dynamical, Statistical–Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall." Monthly Weather Review 139, no. 3 (March 1, 2011): 958–75. http://dx.doi.org/10.1175/2010mwr3399.1.
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Abstract The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.
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Li, Chen, Jing-Jia Luo, Shuanglin Li, Harry Hendon, Oscar Alves, and Craig MacLachlan. "Multimodel Prediction Skills of the Somali and Maritime Continent Cross-Equatorial Flows." Journal of Climate 31, no. 6 (March 2018): 2445–64. http://dx.doi.org/10.1175/jcli-d-17-0272.1.
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Predictive skills of the Somali cross-equatorial flow (CEF) and the Maritime Continent (MC) CEF during boreal summer are assessed using three ensemble seasonal forecasting systems, including the coarse-resolution Predictive Ocean Atmospheric Model for Australia (POAMA, version 2), the intermediate-resolution Scale Interaction Experiment–Frontier Research Center for Global Change (SINTEX-F), and the high-resolution seasonal prediction version of the Australian Community Climate and Earth System Simulator (ACCESS-S1) model. Retrospective prediction results suggest that prediction of the Somali CEF is more challenging than that of the MC CEF. While both the individual models and the multimodel ensemble (MME) mean show useful skill (with the anomaly correlation coefficient being above 0.5) in predicting the MC CEF up to 5-month lead, only ACCESS-S1 and the MME can skillfully predict the Somali CEF up to 2-month lead. Encouragingly, the CEF seesaw index (defined as the difference of the two CEFs as a measure of the negative phase relation between them) can be skillfully predicted up to 4–5 months ahead by SINTEX-F, ACCESS-S1, and the MME. Among the three models, the high-resolution ACCESS-S1 model generally shows the highest skill in predicting the individual CEFs, the CEF seesaw, as well as the CEF seesaw index–related precipitation anomaly pattern in Asia and northern Australia. Consistent with the strong influence of ENSO on the CEFs, the skill in predicting the CEFs depends on the model’s ability in predicting not only the eastern Pacific SST anomaly but also the anomalous Walker circulation that brings ENSO’s influence to bear on the CEFs.
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Rathore, Saurabh, Nathaniel L. Bindoff, Caroline C. Ummenhofer, Helen E. Phillips, Ming Feng, and Mayank Mishra. "Improving Australian Rainfall Prediction Using Sea Surface Salinity." Journal of Climate 34, no. 7 (April 2021): 2473–90. http://dx.doi.org/10.1175/jcli-d-20-0625.1.
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AbstractThis study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer [December–February (DJF)] rainfall over northeastern Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150°E–165°W and 10°S–10°N) and SSSI (50°–95°E and 10°S–10°N)] covaries with Australian rainfall, particularly in the northeast region. Composite analysis that is based on high or low SSS events in the SSSP and SSSI regions is performed to understand the physical links between the SSS and the atmospheric moisture originating from the regions of anomalously high or low, respectively, SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole with anomalously wet conditions over Australia and conversely show the signature of co-occurring El Niño and positive Indian Ocean dipole with anomalously dry conditions there. During the high SSS events of the SSSP and SSSI regions, the convergence of incoming moisture flux results in anomalously wet conditions over Australia with a positive soil moisture anomaly. Conversely, during the low SSS events of the SSSP and SSSI regions, the divergence of incoming moisture flux results in anomalously dry conditions over Australia with a negative soil moisture anomaly. We show from the random-forest regression analysis that the local soil moisture, El Niño–Southern Oscillation (ENSO), and SSSP are the most important precursors for the northeast Australian rainfall whereas for the Brisbane region ENSO, SSSP, and the Indian Ocean dipole are the most important. The prediction of Australian rainfall using random-forest regression shows an improvement by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.
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McCrabb, G. J., and R. A. Hunter. "Prediction of methane emissions from beef cattle in tropical production systems." Australian Journal of Agricultural Research 50, no. 8 (1999): 1335. http://dx.doi.org/10.1071/ar99009.
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The northern beef cattle herd accounts for more than half of Australia’s beef cattle population, and is a major source of anthropogenic methane emissions for Australia. National Greenhouse Gas Inventory predictions of methane output from Australian beef cattle are based on a predictive equation developed for British breeds of sheep and cattle offered temperate forage-based diets. However, tropical forage diets offered to cattle in northern Australia differ markedly from temperate forage-based diets used in the United Kingdom to develop the predictive equations. In this paper we review recent respiration chamber measurements of daily methane production for Brahman cattle offered a tropical forage or high grain diet, and compare them with values predicted using methodologies of the Australian National Greenhouse Gas Inventory Committee and the Intergovernmental Panel on Climate Change. We conclude that a reliable inventory of methane emissions for cattle in northern Australia can only be achieved after a wider range of tropical forage species has been investigated. Some opportunities for reducing methane emissions of beef cattle by dietary manipulation are discussed.
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Wu, Peng, and Yongze Song. "Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia." Remote Sensing 14, no. 6 (March 11, 2022): 1370. http://dx.doi.org/10.3390/rs14061370.
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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.
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Gregory, Paul A., Lawrie J. Rikus, and Jeffrey D. Kepert. "Testing and Diagnosing the Ability of the Bureau of Meteorology’s Numerical Weather Prediction Systems to Support Prediction of Solar Energy Production." Journal of Applied Meteorology and Climatology 51, no. 9 (September 2012): 1577–601. http://dx.doi.org/10.1175/jamc-d-10-05027.1.
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AbstractThe ability of the Australian Bureau of Meteorology’s numerical weather prediction (NWP) systems to predict solar exposure (or insolation) was tested, with the aim of predicting large-scale solar energy several days in advance. The bureau’s Limited Area Prediction System (LAPS) and Mesoscale Assimilation model (MALAPS) were examined for the 2008 calendar year. Comparisons were made with estimates of solar exposure obtained from satellites for the whole Australian continent, as well as site-based exposure observations taken at eight locations across Australia. Monthly-averaged forecast solar exposure over Australia showed good agreement with satellite estimates; the day-to-day exposure values showed some consistent biases, however. Differences in forecast solar exposure were attributed to incorrect representation of convective cloud in the tropics during summer as well as clouds formed by orographic lifting over mountainous areas in southeastern Australia. Comparison with site-based exposure observations was conducted on a daily and hourly basis. The site-based exposure measurements were consistent with the findings from the analysis against satellite data. Hourly analysis at selected sites confirmed that models predicted the solar exposure accurately through low-level clouds (e.g., cumulus), provided that the forecast cloud coverage was accurate. The NWP models struggle to predict solar exposure through middle and high clouds formed by ice crystals (e.g., altocumulus). Sites located in central Australia showed that the monthly-averaged errors in daily solar exposure forecast by the NWP systems were within 5%–10%, up to two days in advance. These errors increased to 20%–30% in the tropics and coastal areas.
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Polkinghorne, R., J. M. Thompson, R. Watson, A. Gee, and M. Porter. "Evolution of the Meat Standards Australia (MSA) beef grading system." Australian Journal of Experimental Agriculture 48, no. 11 (2008): 1351. http://dx.doi.org/10.1071/ea07177.
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The Australian Beef Industry identified variable eating quality as a major contributor to declining beef consumption in the early 1990s and committed research funding to address the problem. The major issue was the ability to predict the eating quality of cooked beef before consumption. The Meat Standards Australia (MSA) program developed a consumer testing protocol, which led to MSA grading standards being defined by consumer score outcomes. Traditional carcass grading parameters proved to be of little value in predicting consumer outcomes. Instead a broader combination of factors forms the basis of an interactive prediction model that performs well. The grading model has evolved from a fixed parameter ‘Pathway’ approach, to a computer model that predicts consumer scores for 135 ‘cut by cooking method’ combinations for each graded carcass. The body of research work conducted in evaluating critical control points and in developing the model predictions and interactions has involved several Australian research groups with strong support and involvement from the industry.
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Speer, MS, LM Leslie, JR Colquhoun, and E. Mitchell. "The Sydney Australia Wildfires of January 1994 - Meteorological Conditions and High Resolution Numerical Modeling Experiments." International Journal of Wildland Fire 6, no. 3 (1996): 145. http://dx.doi.org/10.1071/wf9960145.
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Southeastern Australia is particularly vulnerable to wildfires during the spring and summer months, and the threat of devastation is present most years. In January 1994, the most populous city in Australia, Sydney, was ringed by wildfires, some of which penetrated well into suburban areas and there were many other serious fires in coastal areas of New South Wales (NSW). In recent years much research activity in Australia has focussed on the development of high resolution limited area models, for eventual operational prediction of meteorological conditions associated with high levels of wildfire risk. In this study, the period January 7-8, 1994 was chosen for detailed examination, as it was the most critical period during late December 1993/early January 1994 for the greater Sydney area. Routine forecast guidance from the Australian Bureau of Meteorology's operational numerical weather prediction (NWP) models was very useful in that both the medium and short range models predicted synoptic patterns suggesting extreme fire weather conditions up to several days in advance. However, vital information of a detailed nature was lacking. A new high resolution model was run at the operational resolution of 150 km and the much higher resolutions of 25 km and 5 km. The new model showed statistically significant greater skill in predicting details of wind, relative humidity and temperature patterns both near the surface and above the boundary layer. It also produced skilful predictions of the Forest Fire Danger Index.
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Rahimi, Iman, Amir H. Gandomi, Panagiotis G. Asteris, and Fang Chen. "Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases." Information 12, no. 3 (March 3, 2021): 109. http://dx.doi.org/10.3390/info12030109.
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The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.
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Hendon, Harry H., Eun-Pa Lim, and Guo Liu. "The Role of Air–Sea Interaction for Prediction of Australian Summer Monsoon Rainfall." Journal of Climate 25, no. 4 (February 8, 2012): 1278–90. http://dx.doi.org/10.1175/jcli-d-11-00125.1.
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Abstract Forecast skill for seasonal mean rainfall across northern Australia is lower during the summer monsoon than in the premonsoon transition season based on 25 years of hindcasts using the Predictive Ocean Atmosphere Model for Australia (POAMA) coupled model seasonal forecast system. The authors argue that this partly reflects an intrinsic property of the monsoonal system, whereby seasonally varying air–sea interaction in the seas around northern Australia promotes predictability in the premonsoon season and demotes predictability after monsoon onset. Trade easterlies during the premonsoon season support a positive feedback between surface winds, SST, and rainfall, which results in stronger and more persistent SST anomalies to the north of Australia that compliment the remote forcing of Australian rainfall from El Niño in the Pacific. After onset of the Australian summer monsoon, this local feedback is not supported in the monsoonal westerly regime, resulting in weaker SST anomalies to the north of Australia and with lower persistence than in the premonsoon season. Importantly, the seasonality of this air–sea interaction is captured in the POAMA forecast model. Furthermore, analysis of perfect model forecasts and forecasts generated by prescribing observed SST results in largely the same conclusion (i.e., significantly lower actual and potential forecast skill during the monsoon), thereby supporting the notion that air–sea interaction contributes to intrinsically lower predictability of rainfall during the monsoon.
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Firth, Laura, Martin L. Hazelton, and Edward P. Campbell. "Predicting the Onset of Australian Winter Rainfall by Nonlinear Classification." Journal of Climate 18, no. 6 (March 15, 2005): 772–81. http://dx.doi.org/10.1175/jcli-3291.1.
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Abstract A method for predicting the timing of winter rains is presented, making no assumptions about the functional form of any relationships that may exist. Ideas built on classification and regression trees and machine learning are used to develop robust predictive rules. These methods are applied in a case study to predict the timing of winter rain in five farming towns in the southwest of Western Australia. The variables used to construct the model are mean monthly sea surface temperatures (SSTs) over a 72-cell grid in the Indian Ocean, Perth monthly mean sea level pressure (MSLP), and monthly values of the Southern Oscillation index (SOI). A predictive model is constructed from data over the period 1949–99. This model correctly classifies the onset of the winter rains approximately 80% of the time with SST variables proving to be the most important in deriving the predictions. Further analysis indicates a change point in the mid-1970s, a well-known phenomenon in the region. The prediction rates are significantly worse after 1975. Furthermore, the important region of the Indian Ocean, in terms of SSTs for prediction, moves from the Tropics down toward the Southern Ocean after this date.
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Shi, Li, Harry H. Hendon, Oscar Alves, Jing-Jia Luo, Magdalena Balmaseda, and David Anderson. "How Predictable is the Indian Ocean Dipole?" Monthly Weather Review 140, no. 12 (December 1, 2012): 3867–84. http://dx.doi.org/10.1175/mwr-d-12-00001.1.
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Abstract In light of the growing recognition of the role of surface temperature variations in the Indian Ocean for driving global climate variability, the predictive skill of the sea surface temperature (SST) anomalies associated with the Indian Ocean dipole (IOD) is assessed using ensemble seasonal forecasts from a selection of contemporary coupled climate models that are routinely used to make seasonal climate predictions. The authors assess predictions from successive versions of the Australian Bureau of Meteorology Predictive Ocean–Atmosphere Model for Australia (POAMA 15b and 24), successive versions of the NCEP Climate Forecast System (CFSv1 and CFSv2), the ECMWF seasonal forecast System 3 (ECSys3), and the Frontier Research Centre for Global Change system (SINTEX-F) using seasonal hindcasts initialized each month from January 1982 to December 2006. The lead time for skillful prediction of SST in the western Indian Ocean is found to be about 5–6 months while in the eastern Indian Ocean it is only 3–4 months when all start months are considered. For the IOD events, which have maximum amplitude in the September–November (SON) season, skillful prediction is also limited to a lead time of about one season, although skillful prediction of large IOD events can be longer than this, perhaps up to about two seasons. However, the tendency for the models to overpredict the occurrence of large events limits the confidence of the predictions of these large events. Some common model errors, including a poor representation of the relationship between El Niño and the IOD, are identified indicating that the upper limit of predictive skill of the IOD has not been achieved.
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McKay, Roseanna C., Julie M. Arblaster, and Pandora Hope. "Tropical influence on heat-generating atmospheric circulation over Australia strengthens through spring." Weather and Climate Dynamics 3, no. 2 (April 5, 2022): 413–28. http://dx.doi.org/10.5194/wcd-3-413-2022.
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Abstract. Extreme maximum temperatures during Australian spring can have deleterious impacts on a range of sectors from health to wine grapes to planning for wildfires but are studied relatively little compared to spring rainfall. Spring maximum temperatures in Australia have been rising over recent decades, and it is important to understand how Australian spring maximum temperatures develop in the present and warming climate. Australia's climate is influenced by variability in the tropics and extratropics, but some of this influence impacts Australia differently from winter to summer and, consequently, may have different impacts on Australia as spring evolves. Using linear regression analysis, this paper explores the atmospheric dynamics and remote drivers of high maximum temperatures over the individual months of spring. We find that the drivers of early spring maximum temperatures in Australia are more closely related to low-level wind changes, which in turn are more related to the Southern Annular Mode than variability in the tropics. By late spring, Australia's maximum temperatures are proportionally more related to warming through subsidence than low-level wind changes and more closely related to tropical variability. This increased relationship with the tropical variability is linked with the breakdown of the subtropical jet through spring and an associated change in tropically forced Rossby wave teleconnections. An improved understanding of how the extratropics and tropics project onto the mechanisms that drive high maximum temperatures through spring may lead to improved sub-seasonal prediction of high temperatures in the future.
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Gore, CJ, AJ Crockett, DG Pederson, ML Booth, A. Bauman, and N. Owen. "Spirometric standards for healthy adult lifetime nonsmokers in Australia." European Respiratory Journal 8, no. 5 (May 1, 1995): 773–82. http://dx.doi.org/10.1183/09031936.95.08050773.
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The aim of this study was to develop suitable spirometric prediction equations for asymptomatic Caucasian adults in the Australian population. These equations were compared with those of previous studies and constants were presented which, when associated with the prediction equations, permitted the calculation of 5% tolerance intervals for lung function. The 1,302 subjects (aged 18-78 yrs) who underwent pneumotachograph spirometry, using techniques recommended by the American Thoracic Society, were a sample from metropolitan Adelaide, South Australia. The variables recorded were sex, age, height, mass, forced expiratory volume in one second (FEV1), forced vital capacity (FVC), peak expiratory flow rate (PEFR), forced mid-expiratory flow (FEF25-75%) and FEV1/FVC ratio. Complete data were obtained for 614 females and 621 males, but the sample was reduced to 249 females and 165 males when only lifetime nonsmokers with no adverse bronchial symptoms were selected. Prediction equations of normal lung function were obtained from the reduced sample by multiple regression, with age, height and functions of both age and height as predictors. The derived equations did not differ significantly from the majority of previously reported equations and were generally superior in their ability to predict the lung function of the asymptomatic ex-smokers who were part of the original sample. Analysis of the sensitivity, specificity and predictive power of 5% tolerance limits for the presence of symptoms revealed the important roles of FEV1, FEV1/FVC and FEF25-75% in diagnostic testing. The present prediction equations are recommended for use on the Australian population and on populations with similar Caucasian characteristics.
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Cottrill, Andrew, Harry H. Hendon, Eun-Pa Lim, Sally Langford, Kay Shelton, Andrew Charles, David McClymont, David Jones, and Yuriy Kuleshov. "Seasonal Forecasting in the Pacific Using the Coupled Model POAMA-2." Weather and Forecasting 28, no. 3 (June 1, 2013): 668–80. http://dx.doi.org/10.1175/waf-d-12-00072.1.
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Abstract The development of a dynamical model seasonal prediction service for island nations in the tropical South Pacific is described. The forecast model is the Australian Bureau of Meteorology's Predictive Ocean–Atmosphere Model for Australia (POAMA), a dynamical seasonal forecast system. Using a hindcast set for the period 1982–2006, POAMA is shown to provide skillful forecasts of El Niño and La Niña many months in advance and, because the model faithfully simulates the spatial and temporal variability of rainfall associated with displacements of the southern Pacific convergence zone (SPCZ) and ITCZ during La Niña and El Niño, it also provides good predictions of rainfall throughout the tropical Pacific region. The availability of seasonal forecasts from POAMA should be beneficial to Pacific island countries for the production of regional climate outlooks across the region.
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Langford, Sally, and Harry H. Hendon. "Improving Reliability of Coupled Model Forecasts of Australian Seasonal Rainfall." Monthly Weather Review 141, no. 2 (February 1, 2013): 728–41. http://dx.doi.org/10.1175/mwr-d-11-00333.1.
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Abstract Seasonal rainfall predictions for Australia from the Predictive Ocean Atmosphere Model for Australia (POAMA), version P15b, coupled model seasonal forecast system, which has been run operationally at the Australian Bureau of Meteorology since 2002, are overconfident (too low spread) and only moderately reliable even when forecast accuracy is highest in the austral spring season. The lack of reliability is a major impediment to operational uptake of the coupled model forecasts. Considerable progress has been made to reduce reliability errors with the new version of POAMA2, which makes use of a larger ensemble from three different versions of the model. Although POAMA2 can be considered to be multimodel, its individual models and forecasts are similar as a result of using the same perturbed initial conditions and the same model lineage. Reliability of the POAMA2 forecasts, although improved, remains relatively low. Hence, the authors explore the additional benefit that can be attained using more independent models available in the European Union Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project. Although forecast skill and reliability of seasonal predictions of Australian rainfall are similar for POAMA2 and the ENSEMBLES models, forming a multimodel ensemble using POAMA2 and the ENSEMBLES models is shown to markedly improve reliability of Australian seasonal rainfall forecasts. The benefit of including POAMA2 into this multimodel ensemble is due to the additional information and skill of the independent model, and not just due to an increase in the number of ensemble members. The increased reliability, as well as improved accuracy, of regional rainfall forecasts from this multimodel ensemble system suggests it could be a useful operational prediction system.
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Tanha, Hassan, and Michael Dempsey. "The Information Content of ASX SPI 200 Implied Volatility." Review of Pacific Basin Financial Markets and Policies 19, no. 01 (March 2016): 1650002. http://dx.doi.org/10.1142/s0219091516500028.
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In Australia, the equivalent of a US VIX indicator has recently become available. In response, we consider whether the information captured in the implied volatility of options on the Australian SPI 200 Futures index is superior to the information content of a generalized autoregressive conditional heteroskedasticity (GARCH) approach to volatility prediction. We conclude that the implied volatility of at-the-money (ATM) call options on the SPI 200 Index futures is more powerful, dominating other modes of moneyness options as well as GARCH predictions.
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McKenzie, Neil, and David Jacquier. "Improving the field estimation of saturated hydraulic conductivity in soil survey." Soil Research 35, no. 4 (1997): 803. http://dx.doi.org/10.1071/s96093.
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Prediction of the movement and storage of water in soil is central to quantitative land evaluation. However, spatial and temporal predictions have not been provided by most Australian soil surveys. The saturated hydraulic conductivity (Ks) is an essential parameter for description of water movement in soil and its estimation has been considered too difficult for logistic and technical reasons. The Ks cannot be measured everywhere and relationships with readily observed morphological variables have to be established. However, conventional morphology by itself is a poor predictor of Ks. We have developed a more functional set of morphological descriptors better suited to the prediction of Ks. The descriptors can be applied at several levels of detail. Measurements of functional morphology and Ks were made on 99 horizons from 36 sites across south-eastern Australia. Useful predictions of Ks were possible using field texture, grade of structure, areal porosity, bulk density, dispersion index, and horizon type. A simple visual estimate of areal porosity was satisfactory, although a more quantitative system of measurement provided only slightly better predictions. Regression trees gave more plausible predictive models than standard multiple regressions because they provided a realistic portrayal of the non-additive and conditional nature of the relationships between morphology and Ks. The results are encouraging and indicate that coarse-level prediction of Ks is possible in routine soil survey. Direct measurement of Ks does not appear to be generally feasible because of the high cost, dynamic nature of Ks, and substantial short-range variation in the field. Prediction is further constrained by the limited returns from more sophisticated morphological predictors. The degree to which this limits practical land evaluation is yet to be demonstrated.
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Hudson, Debra, Oscar Alves, Harry H. Hendon, Eun-Pa Lim, Guoqiang Liu, Jing-Jia Luo, Craig MacLachlan, et al. "ACCESS-S1 The new Bureau of Meteorology multi-week to seasonal prediction system." Journal of Southern Hemisphere Earth Systems Science 67, no. 3 (2017): 132. http://dx.doi.org/10.1071/es17009.
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ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size.ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Ocean Dipole, as well as multi-week variations of the Southern Annular Mode and the Madden-Julian Oscillation — all important drivers of Australian climate variability. There is an overall improvement in the skill of the forecasts of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) over Australia on multi-week timescales compared to POAMA. On seasonal timescales the differences between the two systems are generally less marked. ACCESS-S1 has improved seasonal forecasts over Australia for the austral spring season compared to POAMA, with particularly good forecast reliability for rainfall and Tmax. However, forecasts of seasonal mean Tmax are noticeably less skilful over eastern Australia for forecasts of late autumn and winter compared to POAMA.The study has identified scope for improvement of ACCESS-S in the future, particularly 1) reducing rainfall errors in the Indian Ocean and Maritime Continent regions, and 2) initialising the land surface with realistic soil moisture rather than climatology. The latter impacts negatively on the skill of the temperature forecasts over eastern Australia and is being addressed in the next version of the system, ACCESS-S2.
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RANANGGA, TJOK GDE SAHITYAHUTTI, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "METODE VECTOR AUTOREGRESSIVE (VAR) DALAM PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI." E-Jurnal Matematika 7, no. 2 (May 13, 2018): 157. http://dx.doi.org/10.24843/mtk.2018.v07.i02.p198.
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The purposes of this research were to model and to forecast the number of foreign tourists (Australia, China, and Japan) arrival to Bali using vector autoregressive (VAR) method. The estimated of VAR model obtained to forecast the number of foreign tourists to Bali is the sixth order VAR (VAR(6)).We used multivariate least square method to estimate the VAR(6)’s parameters.The mean absolute percentage error (MAPE) in this model were as follows 6.8% in predicting the number of Australian tourists, 15.9% in predicting the number of Chinese tourists, and 9% in predicting the number of Japanese tourists. The prediction of Australian, Chinese, and Japanese tourists arrival to Bali for July 2017 to December 2017 tended to experience up and downs that were not too high compared to the previous months.
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Meehl, Gerald A., Aixue Hu, and Claudia Tebaldi. "Decadal Prediction in the Pacific Region." Journal of Climate 23, no. 11 (June 1, 2010): 2959–73. http://dx.doi.org/10.1175/2010jcli3296.1.
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Abstract A “perfect model” configuration with a global coupled climate model 30-member ensemble is used to address decadal prediction of Pacific SSTs. All model data are low-pass filtered to focus on the low-frequency decadal component. The first three EOFs in the twentieth-century simulation, representing nearly 80% of the total variance, are used as the basis for early twenty-first-century predictions. The first two EOFs represent the forced trend and the interdecadal Pacific oscillation (IPO), respectively, as noted in previous studies, and the third has elements of both trend and IPO patterns. The perfect model reference simulation, the target for the prediction, is taken as the experiment that ran continuously from the twentieth to twenty-first century using anthropogenic and natural forcings for the twentieth century and the A1B scenario for the twenty-first century. The other 29 members use a perturbation in the atmosphere at year 2000 and are run until 2061. Since the IPO has been recognized as a dominant contributor to decadal variability in the Pacific, information late in the twentieth century and early in the twenty-first century is used to select a subset of ensemble members that are more skillful in tracking the time evolution of the IPO (EOF2) in relation to a notional start date of 2010. Predictions for the 19-yr period centered on the year 2020 use that subset of ensemble members to construct Pacific SST patterns based on the predicted evolution of the first three EOFs. Compared to the perfect model reference simulation, the predictions show some skill for Pacific SST predictions with anomaly pattern correlations greater than +0.5. An application of the Pacific SST prediction is made to precipitation over North America and Australia. Even though there are additional far-field influences on Pacific SSTs and North American and Australian precipitation involving the Atlantic multidecadal oscillation (AMO) in the Atlantic, and Indian Ocean and South Asian monsoon variability, there is qualitative skill for the pattern of predicted precipitation over North America and Australia using predicted Pacific SSTs. This exercise shows that, in the presence of a large forced trend like that in the large ensemble, much of Pacific region decadal predictability about 20 years into the future arises from increasing greenhouse gases.
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Cruz, Miguel G., Susan Kidnie, Stuart Matthews, Richard J. Hurley, Alen Slijepcevic, David Nichols, and Jim S. Gould. "Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands." International Journal of Wildland Fire 25, no. 9 (2016): 995. http://dx.doi.org/10.1071/wf16036.
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The moisture content of dead grass fuels is an important input to grassland fire behaviour prediction models. We used standing dead grass moisture observations collected within a large latitudinal spectrum in Eastern Australia to evaluate the predictive capacity of six different fuel moisture prediction models. The best-performing models, which ranged from a simple empirical formulation to a physically based process model, yield mean absolute errors of 2.0% moisture content, corresponding to a 25–30% mean absolute percentage error. These models tended to slightly underpredict the moisture content observations. The results have important implications for the authenticity of fire danger rating and operational fire behaviour prediction, which form the basis of community information and warnings, such as evacuation notices, in Australia.
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Hudson, Debra, Oscar Alves, Harry H. Hendon, Eun-Pa Lim, Guoqiang Liu, Jing-Jia Luo, Craig MacLachlan, et al. "Corrigendum to: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system." Journal of Southern Hemisphere Earth Systems Science 70, no. 1 (2020): 393. http://dx.doi.org/10.1071/es17009_co.
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ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size.ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Ocean Dipole, as well as multi-week variations of the Southern Annular Mode and the Madden-Julian Oscillation — all important drivers of Australian climate variability. There is an overall improvement in the skill of the forecasts of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) over Australia on multi-week timescales compared to POAMA. On seasonal timescales the differences between the two systems are generally less marked. ACCESS-S1 has improved seasonal forecasts over Australia for the austral spring season compared to POAMA, with particularly good forecast reliability for rainfall and Tmax. However, forecasts of seasonal mean Tmax are noticeably less skilful over eastern Australia for forecasts of late autumn and winter compared to POAMA.The study has identified scope for improvement of ACCESS-S in the future, particularly 1) reducing rainfall errors in the Indian Ocean and Maritime Continent regions, and 2) initialising the land surface with realistic soil moisture rather than climatology. The latter impacts negatively on the skill of the temperature forecasts over eastern Australia and is being addressed in the next version of the system, ACCESS-S2.
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Orton, T. G., D. E. Allen, and P. M. Bloesch. "Nitrogen mineralisation in sugarcane soils in Queensland, Australia: II. From laboratory to field-based prediction." Soil Research 57, no. 7 (2019): 755. http://dx.doi.org/10.1071/sr19032.
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Using Australian sugarcane regions as a case study, we present an approach for prediction of in-field nitrogen (N) mineralisation over a crop season. The approach builds on the statistical modelling applied in Allen et al. 2019, which demonstrated good predictive ability on data from a laboratory incubation study (an external R2 of 0.84 in a cross-validation exercise), and adjusts those mineralisation rates according to soil moisture and temperature factors. The required field soil temperature and moisture conditions were simulated using a mechanistic model for the response of soil conditions to input climate data. We investigate drivers of variability in the predicted in-season mineralised N, and compare predictions with currently implemented N fertiliser discounts, which are based on a relationship with soil organic carbon content. The main purpose of this paper is to illustrate the potential use of the results in Allen et al. (2019) for calculating predictions of in-season mineralised N that could be applicable under field conditions in the Australian sugarcane regions. A thorough test to properly validate predictions has not yet been conducted, but collecting data to do so should be the focus of further work.
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Santoso, Agus, Harry Hendon, Andrew Watkins, Scott Power, Dietmar Dommenget, Matthew H. England, Leela Frankcombe, et al. "Dynamics and Predictability of El Niño–Southern Oscillation: An Australian Perspective on Progress and Challenges." Bulletin of the American Meteorological Society 100, no. 3 (March 2019): 403–20. http://dx.doi.org/10.1175/bams-d-18-0057.1.
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AbstractEl Niño and La Niña, the warm and cold phases of El Niño–Southern Oscillation (ENSO), cause significant year-to-year disruptions in global climate, including in the atmosphere, oceans, and cryosphere. Australia is one of the countries where its climate, including droughts and flooding rains, is highly sensitive to the temporal and spatial variations of ENSO. The dramatic impacts of ENSO on the environment, society, health, and economies worldwide make the application of reliable ENSO predictions a powerful way to manage risks and resources. An improved understanding of ENSO dynamics in a changing climate has the potential to lead to more accurate and reliable ENSO predictions by facilitating improved forecast systems. This motivated an Australian national workshop on ENSO dynamics and prediction that was held in Sydney, Australia, in November 2017. This workshop followed the aftermath of the 2015/16 extreme El Niño, which exhibited different characteristics to previous extreme El Niños and whose early evolution since 2014 was challenging to predict. This essay summarizes the collective workshop perspective on recent progress and challenges in understanding ENSO dynamics and predictability and improving forecast systems. While this essay discusses key issues from an Australian perspective, many of the same issues are important for other ENSO-affected countries and for the international ENSO research community.
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Mohseni, Hessam, Sujeeva Setunge, Guomin Zhang, Ruwini Edirisinghe, and Ronald Wakefield. "Deterioration Prediction for Community Buildings in Australia." International Journal of the Constructed Environment 1, no. 4 (2012): 175–96. http://dx.doi.org/10.18848/2154-8587/cgp/v01i04/37498.
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Nelson, Mark R., and Mark Woodward. "Developing cardiovascular risk prediction models for Australia." Medical Journal of Australia 210, no. 4 (February 18, 2019): 158–59. http://dx.doi.org/10.5694/mja2.50010.
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Aijaz, Saima, Jeffrey D. Kepert, Hua Ye, Zhendong Huang, and Alister Hawksford. "Bias Correction of Tropical Cyclone Parameters in the ECMWF Ensemble Prediction System in Australia." Monthly Weather Review 147, no. 11 (November 1, 2019): 4261–85. http://dx.doi.org/10.1175/mwr-d-18-0377.1.
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Abstract Global ensemble prediction systems have considerable ability to predict tropical cyclone (TC) formation and subsequent evolution. However, because of their relatively coarse resolution, their predictions of intensity and structure are biased. The biases arise mainly from underestimated intensities and enlarged radii, in particular the radius of maximum winds. This paper describes a method to reduce this limitation by bias correcting TCs in the ECMWF Ensemble Prediction System (ECMWF-EPS) for a region northwest of Australia. A bias-corrected TC system will provide more accurate forecasts of TC-generated wind and waves to the oil and gas industry, which operates a large number of offshore facilities in the region. It will also enable improvements in response decisions for weather sensitive operations that affect downtime and safety risks. The bias-correction technique uses a multivariate linear regression method to bias correct storm intensity and structure. Special strategies are used to maintain ensemble spread after bias correction and to predict the radius of maximum winds using a climatological relationship based on wind intensity and storm latitude. The system was trained on the Australian best track TC data and the ECMWF-EPS TC data from two cyclone seasons. The system inserts corrected vortices into the original surface wind and pressure fields, which are then used to estimate wind exceedance probabilities, and to drive a wave model. The bias-corrected system has shown an overall skill improvement over the uncorrected ECMWF-EPS for all TC intensity and structure parameters with the most significant gains for the maximum wind speed prediction. The system has been operational at the Australian Bureau of Meteorology since November 2016.
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Gibson, A. J., D. C. Verdon-Kidd, and G. R. Hancock. "Characterising the seasonal nature of meteorological drought onset and termination across Australia." Journal of Southern Hemisphere Earth Systems Science 72, no. 1 (February 8, 2022): 38–51. http://dx.doi.org/10.1071/es21009.
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Drought, and its associated impacts, represents one of the costliest natural hazards worldwide, highlighting the need for prediction and preparedness. While advancements have been made in monitoring current droughts, prediction of onset and termination have proven to be much more challenging. This is because drought is unlike any other natural hazard and cannot be characterised by a single weather event. There is also a high degree of spatial variability in this phenomenon across the vast expanse of the Australian continent. Therefore, by characterising regionally specific expressions of drought, we may improve drought predictability. In this study, we analyse the timing of onset and termination of meteorological droughts across Australia from 1900 to 2015, as well as their local and regional climate controls. We show that meteorological drought onset has a strong seasonal signature across Australia that varies spatially, whereas termination is less seasonally restricted. Using a Random Forest modelling approach with predictor variables representative of large-scale ocean-atmosphere phenomena and local climate, up to 75% of the variance in the Standardised Precipitation Index during both onset and termination could be explained. This study offers support to continued development in long-lead forecasting of local and large-scale ocean/atmosphere conditions to improve drought prediction in Australia and elsewhere.
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Mak, Anita S. "Skilled Hong Kong Immigrants' Intention to Repatriate." Asian and Pacific Migration Journal 6, no. 2 (June 1997): 169–84. http://dx.doi.org/10.1177/011719689700600202.
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An emphasis on skills in Australian immigration policy in the past decade has led to the increase of highly skilled Hong Kong immigrants. However, Australia has not been able to retain all of them. An estimated 30 percent attrition rate among recently arrived Hong Kong-born settlers in Australia is noted by Kee and Skeldon (1994). This paper reports the results of an in-depth study on intention to repatriate and work in Hong Kong, conducted in Australia with 111 professional and managerial Hong Kong immigrants. Correlational and loglinear analyses on prediction of such an intention are presented. Research findings on the career-family dilemma experienced by a number of immigrants are likewise discussed.
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Chen, Yang, Tim R. McVicar, Randall J. Donohue, Nikhil Garg, François Waldner, Noboru Ota, Lingtao Li, and Roger Lawes. "To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction." Remote Sensing 12, no. 10 (May 21, 2020): 1653. http://dx.doi.org/10.3390/rs12101653.
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The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low.
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Hossain, Md Monowar, A. H. M. Faisal Anwar, Nikhil Garg, Mahesh Prakash, and Mohammed Bari. "Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data." Hydrology 9, no. 6 (June 17, 2022): 111. http://dx.doi.org/10.3390/hydrology9060111.
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Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function.
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Aboura, Khalid, and Bijan Samali. "The Information System for Bridge Networks Condition Monitoring and Prediction." International Journal of Information Technologies and Systems Approach 5, no. 1 (January 2012): 1–18. http://dx.doi.org/10.4018/jitsa.2012010101.
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This paper introduces an information system for estimating lifetime characteristics of elements of bridges and predicting the future conditions of networks of bridges. The Information System for Bridge Networks Condition Monitoring and Prediction was developed for the Roads and Traffic Authority of the state of New South Wales, Australia. The conceptual departure from the standard bridge management systems is the use of a novel stochastic process built out of the gamma process. The statistical model was designed for the estimation of infrastructure lifetime, based on the analysis of more than 15 years of bridge inspection data. The predictive curve provides a coherent mathematical model for conducting target level constrained and funding based maintenance optimization.
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Charles, Andrew, Bertrand Timbal, Elodie Fernandez, and Harry Hendon. "Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin." Monthly Weather Review 141, no. 3 (March 1, 2013): 1099–117. http://dx.doi.org/10.1175/mwr-d-12-00098.1.
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Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.
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Hassouna, Fady M. A., and Ian Pringle. "Analysis and Prediction of Crash Fatalities in Australia." Open Transportation Journal 13, no. 1 (September 26, 2019): 134–40. http://dx.doi.org/10.2174/1874447801913010134.
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Introduction: As fatalities, injuries, and economic losses from road accidents are a major concern for governments and their citizens, Australia, like other countries, has designed and implemented a wide range of strategies to reduce the rate of road accidents. Methods: As part of the strategy design process, data on crash deaths were collected and then analyzed to develop more effective strategies. The data of crash deaths in Australia during the years 1965 to 2018 were analyzed based on gender, causes of crash deaths, and type of road users, and then the results were compared with global averages, then a prediction model was developed to forecast the future annual crash fatalities. Results: The results indicate that, based on gender, the rate of male road fatalities in Australia was significantly higher than that of female road fatalities. Whereas based on the cause of death, the first cause of death was over speeding. Based on the type of road users, the drivers and passengers of 4-wheel vehicles had the highest rate of fatalities. Conclusion: The prediction model was developed based on Autoregressive Integrated Moving Average (ARIMA) methodology, and annual road fatalities in Australia for the next five years 2019-2022 have been forecast using this model.
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Hudson, Debra, Andrew G. Marshall, Yonghong Yin, Oscar Alves, and Harry H. Hendon. "Improving Intraseasonal Prediction with a New Ensemble Generation Strategy." Monthly Weather Review 141, no. 12 (November 25, 2013): 4429–49. http://dx.doi.org/10.1175/mwr-d-13-00059.1.
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Abstract The Australian Bureau of Meteorology has recently enhanced its capability to make coupled model forecasts of intraseasonal climate variations. The Predictive Ocean Atmosphere Model for Australia (POAMA, version 2) seasonal prediction forecast system in operations prior to March 2013, designated P2-S, was not designed for intraseasonal forecasting and has deficiencies in this regard. Most notably, the forecasts were only initialized on the 1st and 15th of each month, and the growth of the ensemble spread in the first 30 days of the forecasts was too slow to be useful on intraseasonal time scales. These deficiencies have been addressed in a system upgrade by initializing more often and through enhancements to the ensemble generation. The new ensemble generation scheme is based on a coupled-breeding approach and produces an ensemble of perturbed atmosphere and ocean states for initializing the forecasts. This scheme impacts favorably on the forecast skill of Australian rainfall and temperature compared to P2-S and its predecessor (version 1.5). In POAMA-1.5 the ensemble was produced using time-lagged atmospheric initial conditions but with unperturbed ocean initial conditions. P2-S used an ensemble of perturbed ocean initial conditions but only a single atmospheric initial condition. The improvement in forecast performance using the coupled-breeding approach is primarily reflected in improved reliability in the first month of the forecasts, but there is also higher skill in predicting important drivers of intraseasonal climate variability, namely the Madden–Julian oscillation and southern annular mode. The results illustrate the importance of having an optimal ensemble generation strategy.
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Pang, Alexis, Melissa W. L. Chang, and Yang Chen. "Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia." Sensors 22, no. 3 (January 18, 2022): 717. http://dx.doi.org/10.3390/s22030717.
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Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R2 = 0.86, RMSE = 0.18 t ha−1). RF models for individual paddocks in VIC (R2 = 0.89, RMSE = 0.15 t ha−1) and NSW (R2 = 0.87, RMSE = 0.07 t ha−1) performed well, but moderate performance was seen for SA (R2 = 0.45, RMSE = 0.25 t ha−1). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction.
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Brinkhoff, James, and Andrew J. Robson. "Macadamia Orchard Planting Year and Area Estimation at a National Scale." Remote Sensing 12, no. 14 (July 13, 2020): 2245. http://dx.doi.org/10.3390/rs12142245.
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Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold.
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Graser, H.-U., B. Tier, D. J. Johnston, and S. A. Barwick. "Genetic evaluation for the beef industry in Australia." Australian Journal of Experimental Agriculture 45, no. 8 (2005): 913. http://dx.doi.org/10.1071/ea05075.
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Genetic evaluation for beef cattle in Australia has been performed using an animal model with best linear unbiased prediction since 1984. The evaluation procedures have evolved from simple to more complex models and from few to a large number of traits, including traits for reproduction, growth and carcass characteristics. This paper describes in detail the current beef cattle genetic evaluation system ‘BREEDPLAN’ used for the Australian beef cattle industry, the traits analysed and underlying models, and presents a short overview of the challenges and planned developments of coming years.
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Sarasa-Cabezuelo, Antonio. "Prediction of Rainfall in Australia Using Machine Learning." Information 13, no. 4 (March 24, 2022): 163. http://dx.doi.org/10.3390/info13040163.
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Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic models are used that offer predictions with a margin of error, so that in many cases they are not very good. Due to the aforementioned conditions, the use of machine learning algorithms can serve to improve predictions. This article describes an exploratory study of the use of machine learning to make predictions about the phenomenon of rain. To do this, a set of data was taken as an example that describes the measurements gathered on rainfall in the main cities of Australia in the last 10 years, and some of the main machine learning algorithms were applied (knn, decision tree, random forest, and neural networks). The results show that the best model is based on neural networks.
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WOOD, M. J., R. Scott, P. W. Volker, and D. J. Mannes. "Windthrow In Tasmania, Australia: Monitoring, Prediction And Management." Forestry 81, no. 3 (March 22, 2008): 415–27. http://dx.doi.org/10.1093/forestry/cpn005.
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Weinzierl, Bernadett, Roger K. Smith, Michael J. Reeder, and Gordon E. Jackson. "MesoLAPS Predictions of Low-Level Convergence Lines over Northeastern Australia." Weather and Forecasting 22, no. 4 (August 1, 2007): 910–27. http://dx.doi.org/10.1175/waf1018.1.
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Abstract The prediction of low-level convergence lines over northeastern Australia such as those which give rise to the “morning glory” phenomenon and the north Australian cloud line (NACL) are investigated using MesoLAPS, a mesoscale version of the Australian Bureau of Meteorology’s operational Limited Area Prediction System. The model is used also to examine aspects of the dynamics of such lines. The predictions were made during the Gulf Lines Experiment in 2002 and are compared here with data collected during the experiment. The ability of MesoLAPS to forecast the convergence lines is investigated in detail for selected cases. In two cases with well-developed southerly morning glory disturbances, the model was able to capture the separation of a borelike disturbance from an airmass change, although the model does not have the resolution to capture the wavelike structures that develop at the leading edge of the bore waves. An analysis of the entire 44-day period between 11 September and 24 October shows that MesoLAPS has significant skill in forecasting the lines, but it does not capture all of them. About 85% of forecasts of northeasterly morning glories and southerly morning glories, or of their nonoccurrence, were correct, while the corresponding percentage for the NACL was about 65%. However, about 15% of northeasterly morning glories and about 35% of NACL events that occurred were not forecast by the model. Also, only 6 out of 11 southerly morning glories were forecast. A detailed analysis of the MesoLAPS calculations indicates that the broad-scale generation mechanisms of northeasterly and southerly morning glories are similar and it enables the construction of a conceptual model for the generation of southerly morning glories.
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Cubitt, Timothy, Ken Wooden, Erin Kruger, and Michael Kennedy. "A predictive model for serious police misconduct by variation of the theory of planned behaviour." Journal of Forensic Practice 22, no. 4 (November 16, 2020): 251–63. http://dx.doi.org/10.1108/jfp-08-2020-0033.
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Purpose Misconduct and deviance amongst police officers are substantial issues in policing around the world. This study aims to propose a prediction model for serious police misconduct by variation of the theory of planned behaviour. Design/methodology/approach Using two data sets, one quantitative and one qualitative, provided by an Australian policing agency, a random forest analysis and a qualitative content analysis was performed. Results were used to inform and extend the framework of the theory of planned behaviour. The traditional and extended theory of planned behaviour models were then tested for predictive utility. Findings Each model demonstrated noteworthy predictive power, however, the extended model performed particularly well. Prior instances of minor misconduct amongst officers appeared important in this rate of prediction, suggesting that remediation of problematic behaviour was a substantial issue amongst misconduct prone officers. Practical implications It is an important implication for policing agencies that prior misconduct was predictive of further misconduct. A robust complaint investigation and remediation process are pivotal to anticipating, remediating and limiting police misconduct, however, early intervention models should not be viewed as the panacea for police misconduct. Originality/value This research constitutes the first behavioural model for police misconduct produced in Australia. This research seeks to contribute to the field of behavioural prediction amongst deviant police officers, and offer an alternative methodology for understanding these behaviours.
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Hudson, D., A. G. Marshall, O. Alves, G. Young, D. Jones, and A. Watkins. "Forewarned is Forearmed: Extended-Range Forecast Guidance of Recent Extreme Heat Events in Australia." Weather and Forecasting 31, no. 3 (April 29, 2016): 697–711. http://dx.doi.org/10.1175/waf-d-15-0079.1.
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Abstract There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.
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Senanayake, Sameera, Adrian Barnett, Nicholas Graves, Helen Healy, Keshwar Baboolal, and Sanjeewa Kularatna. "Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study." F1000Research 8 (October 29, 2019): 1810. http://dx.doi.org/10.12688/f1000research.20661.1.
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Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1st, 2007, to December 31st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models. The best predictive model will be selected based on the model’s performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
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Senanayake, Sameera, Adrian Barnett, Nicholas Graves, Helen Healy, Keshwar Baboolal, and Sanjeewa Kularatna. "Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study." F1000Research 8 (March 9, 2020): 1810. http://dx.doi.org/10.12688/f1000research.20661.2.
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Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model’s performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
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Becker, Daniel J., Alex D. Washburne, Christina L. Faust, Erin A. Mordecai, and Raina K. Plowright. "The problem of scale in the prediction and management of pathogen spillover." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20190224. http://dx.doi.org/10.1098/rstb.2019.0224.
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Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.
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Pokhrel, P., D. E. Robertson, and Q. J. Wang. "A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions." Hydrology and Earth System Sciences Discussions 9, no. 10 (October 2, 2012): 11199–225. http://dx.doi.org/10.5194/hessd-9-11199-2012.
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Abstract. Hydrological post-processors refer here to statistical models that are applied to hydrological model predictions to further reduce prediction errors and to quantify remaining uncertainty. For streamflow predictions, post-processors are generally applied to daily or sub-daily time scales. For many applications such as seasonal streamflow forecasting and water resources assessment, monthly volumes of streamflows are of primary interest. While it is possible to aggregate post-processed daily or sub-daily predictions to monthly time scales, the monthly volumes so produced may not have the least errors achievable and may not be reliable in uncertainty distributions. Post-processing directly at the monthly time scale is likely to be more effective. In this study, we investigate the use of a Bayesian joint probability modelling approach to directly post-process model predictions of monthly streamflow volumes. We apply the BJP post-processor to 18 catchments located in eastern Australia and demonstrate its effectiveness in reducing prediction errors and quantifying prediction uncertainty.
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Tan, Samson, Khalid Moinuddin, and Paul Joseph. "The Ignition Frequency of Structural Fires in Australia from 2012 to 2019." Fire 6, no. 1 (January 16, 2023): 35. http://dx.doi.org/10.3390/fire6010035.
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Appropriate estimates of ignition frequency derived from fire statistics are crucial for quantifying fire risks, given that ignition frequency underpins all probabilistic fire risk assessments for buildings. Rahikainen et al. (Fire Technol 2004; 40:335–53) utilized the generalized Barrois model to evaluate ignition frequencies for different buildings in Finland. The Barrois model provides a good prediction of the trend of the ignition frequency; however, it can underestimate the ignition frequency depending on the building type. In this study, an analysis of the Australian fire statistical data from 2012 to 2019 was performed and compared with studies from Finland. A new coefficient is proposed to improve the Barrois model for a better fit for buildings in Australia. Several categories, such as hotels and hospitals, which were absent in previous studies, have been included as separate categories in this study. Office and retail spaces in Finland have an ignition frequency one order of magnitude lower than in Australia. On the other hand, other buildings (retail and apartments in particular) are much more prone to fire ignition in Australia than in Finland. The improved generalized Barrois model based on the Australian fire statistical data will be useful for determining ignition frequency for risk quantification in the Australian context.