Journal articles on the topic 'Meteorology Australia'
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1
Grotz, Reinhold. "Australian Government, Bureau of Meteorology, ed, Climate of Australia." Zeitschrift für Australienstudien / Australian Studies Journal 25 (2011): 118–21. http://dx.doi.org/10.35515/zfa/asj.25/2011.12.
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Zillman, John. "Von Neumayer and the origins of Australian and international meteorology." Proceedings of the Royal Society of Victoria 123, no. 1 (2011): 70. http://dx.doi.org/10.1071/rs11070.
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Georg von Neumayer played a central role in building the foundations of Australian meteorology and in shaping the global framework of cooperation under the International Meteorological Organization (IMO), the forerunner of the World Meteorological Organization (WMO). Though his time in Australia was relatively brief, his name stands alongside those of Lieutenant William Dawes (active from 1788-1791), Sir Thomas Brisbane (1822-24), Robert Ellery (1863-1895), Sir Charles Todd (1856-1906), Clement Wragge (1883-1903) and Henry Chamberlain Russell (1859-1904) in the short list of Australia’s outstanding meteorological pioneers; and with Lt. Matthew Fontaine Maury, Admiral Robert FitzRoy and Professors C.H.D. Buys Ballot, H. Wild and E. Mascart in building the 19th century framework for international cooperation in meteorology, especially through his role as President of the International Polar Commission which organised the First International Polar Year (1882-83). This paper provides a brief overview of the origins of Australian meteorology and of the 1873 establishment and early work of the IMO in providing the international framework for cooperation in meteorology until its replacement by the intergovernmental WMO in 1950.
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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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Gamble, Felicity, Grant Beard, Andrew Watkins, David Jones, Catherine Ganter, Vanessa Webb, and Alex Evans. "Tracking the El Nino-Southern Oscillation in real-time: a staged communication approach to event onset." Journal of Southern Hemisphere Earth Systems Science 67, no. 2 (2017): 64. http://dx.doi.org/10.1071/es17006.
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Communicating the development of El Niño and La Niña events is often challenging, largely due to the general misconception that the transition to an event can occur rapidly – like flicking a switch. Additionally, in Australia the association of El Niño with drought, and La Niña with flood, and the impacts that result, can often cloud the message. The Australian Bureau of Meteorology is responsible for issuing updates on the current status of the El Niño-Southern Oscillation (ENSO), and declaring the onset and demise of an event. To assist in the communication of this often complex message, the Bureau of Meteorology developed an online tool, the ENSO Outlook, to keep stakeholders informed of the potential for El Niño or La Niña to develop in the upcoming seasons.
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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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Edwards, P. G. "Charles Todd and the Adelaide Observatory." Publications of the Astronomical Society of Australia 10, no. 4 (1993): 349–54. http://dx.doi.org/10.1017/s1323358000026023.
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AbstractCharles Todd was the first Government Astronomer and Superintendent of Telegraphs in South Australia. Most widely known for his instrumental role in the construction of the Overland Telegraph, linking Australia and England, Todd also established the Adelaide Observatory and made valuable contributions to both astronomy and meteorology.
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Robin, Libby, Steve Morton, and Mike Smith. "Writing a History of Scientific Endeavour in Australia’s Deserts." Historical Records of Australian Science 25, no. 2 (2014): 143. http://dx.doi.org/10.1071/hr14011.
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This special issue of Historical Records of Australian Science explores some of the sciences that have contributed to our understanding of inland Australia, country variously known as desert, the arid zone, drylands and the outback. The sciences that have concentrated on deserts include ecology, geomorphology, hydrology, rangeland management, geography, surveying, meteorology and geology, plus many others. In recognition that desert science has surged ahead in the past few decades, we have invited contributors who describe various different desert initiatives. We use these case studies to open up the discussion about how Australians see their desert lands, how this has changed over time and how desert scientists from the rest of the world regard the distinctive desert country in Australia.
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Tapper, NJ, G. Garden, J. Gill, and J. Fernon. "The Climatology and Meteorology of High Fire Danger in the Northern Territory." Rangeland Journal 15, no. 2 (1993): 339. http://dx.doi.org/10.1071/rj9930339.
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In most areas of Australia the calculation of a fire danger index (FDI) is the cornerstone of fie weather forecasting and provides an operationally objective basis for the issue of fire weather warnings. FDI's are derived from the observation or prediction of a number of basic meteorological parameters which are then combined with information on fuel characteristics. The forest and grassland fire danger in southern Australia is greatest during the austral summer and is characterised by long periods of low fire danger interspersed with occasional extreme fire danger events. By contrast, much of tropical and subtropical Australia shows a distinctly different seasonality, magnitude and frequency of fire danger. The problem is essentially one of the austral winter-spring (dry season) period and is characterised by extended periods of moderate to high fire danger. This paper provides a broad climatological background to the problem of high fire danger in northern Australia, concentrating in particular on the Northern Territory. The paper also addresses particular meteorological situations in northern Australia which give rise to elevated fire danger. Two synoptic-scale weather patterns are discussed in particular; the passage of prefrontal troughs which seasonally produce high fire danger in the region of the tropic, and winter subtropical ridging which produces strong winds and high fire danger over the north of the continent during the dry season.
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Crawford, Jagoda, Scott Chambers, David D. Cohen, Alastair Williams, Alan Griffiths, Eduard Stelcer, and Leisa Dyer. "Impact of meteorology on fine aerosols at Lucas Heights, Australia." Atmospheric Environment 145 (November 2016): 135–46. http://dx.doi.org/10.1016/j.atmosenv.2016.09.025.
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Fowler, Keirnan J. A., Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C. Peel. "CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia." Earth System Science Data 13, no. 8 (August 6, 2021): 3847–67. http://dx.doi.org/10.5194/essd-13-3847-2021.
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Abstract. This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) comprises data for 222 unregulated catchments, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large-scale changes, such as significant changes in land use. Rating curve uncertainty estimates are provided for most (75 %) of the catchments, and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from https://doi.org/10.1594/PANGAEA.921850 (Fowler et al., 2020a).
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Marshall, John Le, Yi Xiao, David Howard, Chris Tingwell, Jeff Freeman, Paul Gregory, Tan Le, et al. "First results from the generation and assimilation of 10-minute atmospheric motion vectors in the Australian region." Journal of Southern Hemisphere Earth Systems Science 66, no. 1 (2016): 12. http://dx.doi.org/10.1071/es16003.
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Ten-minute interval infrared and visible satellite images, available from MTSAR-1R (Himawari-6) for a limited period over and around Australia, have been used to generate atmospheric motion vectors. These vectors—which were available every 10 minutes—have been quality controlled, error characterised and assimilated into the Australian Bureau of Meteorology's next generation operational regional forecast model as part of the new operational database. Results from this study indicate that this high temporal resolution imagery has the ability to produce high spatial and temporal density atmospheric motion vectors, and the quality of these vectors is such that they have the potential to improve numerical analysis and prognosis.
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BI, P., J. E. HILLER, A. S. CAMERON, Y. ZHANG, and R. GIVNEY. "Climate variability and Ross River virus infections in Riverland, South Australia, 1992–2004." Epidemiology and Infection 137, no. 10 (March 19, 2009): 1486–93. http://dx.doi.org/10.1017/s0950268809002441.
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SUMMARYRoss River virus (RRV) infection is the most common notifiable vector-borne disease in Australia, with around 6000 cases annually. This study aimed to examine the relationship between climate variability and notified RRV infections in the Riverland region of South Australia in order to set up an early warning system for the disease in temperate-climate regions. Notified data of RRV infections were collected by the South Australian Department of Health. Climatic variables and monthly river flow were provided by the Australian Bureau of Meteorology and South Australian Department of Water, Land and Biodiversity Conservation over the period 1992–2004. Spearman correlation and time-series-adjusted Poisson regression analysis were performed. The results indicate that increases in monthly mean minimum and maximum temperatures, monthly total rainfall, monthly mean Southern Oscillation Index and monthly flow in the Murray River increase the likelihood, but an increase in monthly mean relative humidity decreases the likelihood, of disease transmission in the region, with different time-lag effects. This study demonstrates that a useful early warning system can be developed for local regions based on the statistical analysis of readily available climate data. These early warning systems can be utilized by local public health authorities to develop disease prevention and control activities.
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Parker, Albert, and Clifford D. Ollier. "Discussion of the “Hottest Year on Record” in Australia." Quaestiones Geographicae 36, no. 1 (March 1, 2017): 79–91. http://dx.doi.org/10.1515/quageo-2017-0006.
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Abstract The global temperature trends provided by the Australian Bureau of Meteorology are artificially exaggerated due to subjective and unidirectional adjustments of recorded values. The present paper aims to promote the use of the raw stations’ data corrected only for urban heat island formation. The longer temperature records of Australia exhibit significant oscillations with a strong quasi-60 years’ signature of downward phases 1880 to 1910, 1940 to 1970 and 2000 to present, and upwards phases 1910 to 1940 and 1970 to 2000. A longer oscillation with downward phase until 1910 and an upwards phase afterwards is also detected. The warming since 1910 occurred at a nearly constant rate. Over the full length of the long Australian records since the end of the 1800s, there is no sign of warming or increased occurrence of extreme events. The monthly highest and mean maximum temperatures do not exhibit any positive trend. The differences between monthly highest and lowest, or monthly mean maximum and mean minimum temperatures, are all reducing because of urban heat island formation.
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Le Marshall, John, Robert Norman, David Howard, Susan Rennie, Michael Moore, Jan Kaplon, Yi Xiao, et al. "Corrigendum to: Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system." Journal of Southern Hemisphere Earth Systems Science 70, no. 1 (2020): 394. http://dx.doi.org/10.1071/es19009_co.
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The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.
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Marshall, John Le, Robert Norman, David Howard, Susan Rennie, Michael Moore, Jan Kaplon, Yi Xiao, et al. "Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system." Journal of Southern Hemisphere Earth Systems Science 69, no. 1 (2019): 161. http://dx.doi.org/10.1071/es19009.
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The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.
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Pook, M. J., J. S. Risbey, P. C. McIntosh, C. C. Ummenhofer, A. G. Marshall, and G. A. Meyers. "The Seasonal Cycle of Blocking and Associated Physical Mechanisms in the Australian Region and Relationship with Rainfall." Monthly Weather Review 141, no. 12 (November 25, 2013): 4534–53. http://dx.doi.org/10.1175/mwr-d-13-00040.1.
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Abstract The seasonal cycle of blocking in the Australian region is shown to be associated with major seasonal temperature changes over continental Antarctica (approximately 15°–35°C) and Australia (about 8°–17°C) and with minor changes over the surrounding oceans (below 5°C). These changes are superimposed on a favorable background state for blocking in the region resulting from a conjunction of physical influences. These include the geographical configuration and topography of the Australian and Antarctic continents and the positive west to east gradient of sea surface temperature in the Indo-Australian sector of the Southern Ocean. Blocking is represented by a blocking index (BI) developed by the Australian Bureau of Meteorology. The BI has a marked seasonal cycle that reflects seasonal changes in the strength of the westerly winds in the midtroposphere at selected latitudes. Significant correlations between the BI at Australian longitudes and rainfall have been demonstrated in southern and central Australia for the austral autumn, winter, and spring. Patchy positive correlations are evident in the south during summer but significant negative correlations are apparent in the central tropical north. By decomposing the rainfall into its contributions from identifiable synoptic types during the April–October growing season, it is shown that the high correlation between blocking and rainfall in southern Australia is explained by the component of rainfall associated with cutoff lows. These systems form the cyclonic components of blocking dipoles. In contrast, there is no significant correlation between the BI and rainfall from Southern Ocean fronts.
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Vizard, A. L., G. A. Anderson, and D. J. Buckley. "Verification and value of the Australian Bureau of Meteorology township seasonal rainfall forecasts in Australia, 1997–2005." Meteorological Applications 12, no. 04 (December 16, 2005): 343. http://dx.doi.org/10.1017/s135048270500191x.
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Lim, Eun-Pa, Harry H. Hendon, Debra Hudson, Guomin Wang, and Oscar Alves. "Dynamical Forecast of Inter–El Niño Variations of Tropical SST and Australian Spring Rainfall." Monthly Weather Review 137, no. 11 (November 1, 2009): 3796–810. http://dx.doi.org/10.1175/2009mwr2904.1.
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Abstract The relationship between variations of Indo-Pacific sea surface temperatures (SSTs) and Australian springtime rainfall over the last 30 years is investigated with a focus on predictability of inter–El Niño variations of SST and associated rainfall anomalies. Based on observed data, the leading empirical orthogonal function (EOF) of Indo-Pacific SST represents mature El Niño conditions, while the second and fourth modes depict major east–west shifts of individual El Niño events. These higher-order EOFs of SST explain more rainfall variance in Australia, especially in the southeast, than does the El Niño mode. Furthermore, intense springtime droughts tend to be associated with peak warming in the central Pacific, as captured by EOFs 2 and 4, together with warming in the eastern Pacific as depicted by EOF1. The ability to predict these inter–El Niño variations of SST and Australian rainfall is assessed with the Australian Bureau of Meteorology dynamical coupled model seasonal forecast system, the Predictive Ocean and Atmospheric Model for Australia (POAMA). A 10-member ensemble of 9-month hindcasts was generated for the period 1980–2006. For the September–November season, the leading 2 EOFs of SST are predictable with lead times of 3–6 months, while SST EOF4 is predictable out to a lead time of 1 month. The teleconnection between the leading EOFs of SST and Australian rainfall is also well depicted in the model. Based on this ability to predict major east–west variations of El Niño and the teleconnection to Australian rainfall, springtime rainfall over eastern Australia, and major drought events are predictable up to a season in advance.
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Bettio, Lynette, John R. Nairn, Steven C. McGibbony, Pandora Hope, Andrew Tupper, and Robert J. B. Fawcett. "A heatwave forecast service for Australia." Proceedings of the Royal Society of Victoria 131, no. 1 (2019): 53. http://dx.doi.org/10.1071/rs19006.
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The Australian Bureau of Meteorology monitors, researches, predicts and communicates Australia’s weather and climate. Australia’s mean temperature has risen by over 1°C since 1910, leading to an increase in the frequency of extreme heat events. Extreme heat can profoundly impact human health, infrastructure and the environment. Research conducted at the Bureau and elsewhere shows that climate change is impacting the intensity and frequency of extreme heat events. One way that the Bureau has responded to this challenge is by providing a forecast service specifically targeted at identifying heatwaves. The heatwave service identifies areas expected to be impacted by three or more consecutive days of unusually high maximum and minimum temperatures on a national map. The service has been developed with clear impact-based categories of heatwave severity. This heatwave service is now available operationally on the Bureau’s website during the heatwave season (nominally November to March) and is proving a valuable tool for engaging the community, including emergency services, with forecasts and warnings of extreme heat.
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Jackson, Stephen. "Thornthwaite Moisture Index and Climate Zones in the Northern Territory." Australian Geomechanics Journal 57, no. 3 (September 1, 2022): 69–85. http://dx.doi.org/10.56295/agj5733.
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The Thornthwaite Moisture Index (TMI) is an established climate parameter for geotechnical engineers to categorise a site and enable estimation of seasonal ground movements associated with soil moisture changes. TMI assessment and mapping for the Northern Territory are presented, using the TMI calculation method commonly used for similar recent studies elsewhere in Australia. The assessment included the analysis of 17 sites within the Northern Territory and one site in Queensland which has enabled development of Climate Zone classifications. Climate data was obtained from the Australian Bureau of Meteorology to calculate the TMI on a ‘year by year’ basis over a target period of 29 years (1990 to 2019). Related work in Queensland (Fox 2002) and Western Australia (Hu et al, 2016) has guided the development of the Northern Territory Climate Zone Map. Further work is required to characterise the soil moisture behaviour in arid zones. A general lack of guidance in AS2870 (2011) for arid areas, including much of the Northern Territory, could be addressed with further research and development.
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Garratt, J. R., E. K. Webb, and S. McCarthy. "Charles Henry Brian Priestley. 8 July 1915 — 18 May 1998." Biographical Memoirs of Fellows of the Royal Society 57 (January 2011): 349–78. http://dx.doi.org/10.1098/rsbm.2011.0015.
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Charles Henry Brian Priestley was born and educated in England. After completing the Mathematical Tripos at the University of Cambridge, he joined the Meteorological Office in 1939. For the next seven years he was engaged mostly in wartime work, including a two-year spell in Canada (1941–43) and three years with the Meteorological Office upper-air unit at Dunstable, UK (1943–46). In 1946, aged 31 years, he took up an Australian appointment with the Council for Scientific and Industrial Research (later to become the Commonwealth Scientific and Industrial Research Organization (CSIRO)) to establish and develop a group to undertake research in meteorological physics. Thereafter he was based in Melbourne, Australia, with his career in the CSIRO extending to 1977. Priestley’s own early research focused on large-scale atmospheric systems, including substantial work on global-scale transport, and later on small-scale atmospheric convection and heat transfer, in which he established some significant results. He had a leading role in the development of the atmospheric sciences in Australia, and was strongly involved in international meteorology.
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Kala, Jatin, Alyce Sala Tenna, Daniel Rudloff, Julia Andrys, Ole Rieke, and Thomas J. Lyons. "Evaluation of the Weather Research and Forecasting model in simulating fire weather for the south-west of Western Australia." International Journal of Wildland Fire 29, no. 9 (2020): 779. http://dx.doi.org/10.1071/wf19111.
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The Weather Research and Forecasting (WRF) model was used to simulate fire weather for the south-west of Western Australia (SWWA) over multiple decades at a 5-km resolution using lateral boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim reanalysis. Simulations were compared with observations at Australian Bureau of Meteorology meteorological stations and the McArthur Forest Fire Danger Index (FFDI) was used to quantify fire weather. Results showed that, overall, the WRF reproduced the annual cumulative FFDI at most stations reasonably well, with most biases in the FFDI ranging between –600 and 600. Biases were highest at stations within the metropolitan region. The WRF simulated the geographical gradients in the FFDI across the domain well. The source of errors in the FFDI varied markedly between the different stations, with no one particular variable able to account for the errors at all stations. Overall, this study shows that the WRF is a useful model for simulating fire weather for SWWA, one of the most fire-prone regions in Australia.
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Post, David, Peter Baker, and Damian Barrett. "Determining the impacts of coal seam gas extraction on water-dependent assets." APPEA Journal 56, no. 2 (2016): 545. http://dx.doi.org/10.1071/aj15051.
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Many Australians, particularly in rural areas, are seeking clear scientific information about the potential impacts of coal seam gas production on groundwater and surface water across the country. In response to the resultant community concern, the Australian Government commissioned an ambitious multi-disciplinary program of bioregional assessments to improve understanding of the potential impacts of coal seam gas (and large coal mining) activities on water-dependent assets across six bioregions in eastern and central Australia. Delivered through a collaboration between the Department of the Environment, the Bureau of Meteorology, CSIRO, and Geoscience Australia—and including close engagement with natural resource management and catchment management organisations, coal resource companies, Indigenous peoples and state governments—the results will allow coal resource companies, governments, and the community to focus on the areas where impacts may occur so that these can be minimised. Key findings of the program will be presented with specific reference to the potential impacts on water-dependent assets due to CSG development by Metgasco and AGL in the Clarence-Moreton and Gloucester regions, respectively.
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Home, R. W. "Neumayer, Humboldt and the search for a global physics." Proceedings of the Royal Society of Victoria 123, no. 1 (2011): 2. http://dx.doi.org/10.1071/rs11002.
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In setting up the Flagstaff Observatory in Melbourne in 1857, the young German geophysicist Georg Neumayer brought new standards of precision to the pursuit of physics in Australia. His wide-ranging research program in geomagnetism, meteorology and oceanography was conceived within an overall approach to science associated especially with the name of Alexander von Humboldt, that saw the Earth and its oceans and atmosphere as an integrated dynamical system. Neumayer also, however, envisaged immediate practical outcomes from his work, whether in determining optimal sailing routes between Europe and Australia, or in locating new mineral deposits. From a personal point of view he regarded his seven years in Australia as, above all, a preparation for the scientific investigation of Antarctica that he dreamed in vain of undertaking.
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Tongkukut, Seni Herlina J. "El-NINO DAN PENGARUHNYA TERHADAP CURAH HUJAN DI MANADO SULAWESI UTARA." JURNAL ILMIAH SAINS 11, no. 1 (April 1, 2011): 102. http://dx.doi.org/10.35799/jis.11.1.2011.51.
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Telah dilakukan analisis El-Nino dan pengaruhnya terhadap curah hujan di Manado Sulut dengan menggunakan data curah hujan bulanan dan Southern Oscillation Index SOI selama thn 1999-2009. Data curah hujan diperoleh dari BMKG Kayuwatu Manado Sulut dan data SOI diunduh dari website Biro Meteorologi Australia BoM. Analisis dilakukan dengan analisis regresi linear sederhana. Diperoleh hasil bahwa curah hujan kota Manado secara umum dari thn 1999-2008 dipengaruhi oleh SOI namun pada thn 2009 ketika terjadi El-nino, curah hujan bulanan Manado tidak dipengaruhi oleh efek El-nino. Hal ini karena pada saat yang sama suhu muka laut perairan Indonesia juga menghangat. EL-NINO AND ITS EFFECT ON RAINFALL IN MANADONORTH SULAWESIABSTRACTAnalysis of El-Nino and its effect on rainfall in Manado, North Sulawesi, using monthly rainfall data and the Southern Oscillation Index SOI during the years 1999-2009 has been carried out. Rainfall data obtained from BMKG Kayuwatu Manado and SOI data downloaded from the website of Australian Bureau of Meteorology (BoM). The analysis was performed with simple linear regression analysis. The results obtained indicate that rainfall in Manado , in general, was influenced by SOI from the years 1999-2008, but when there is an El-Nino in 2009, monthly rainfall in Manado is not affected by the El-Nino effect. This is due to, at the same time, sea surface temperature in Indonesian territory are also warm.
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Jacobs, Stephanie J., Ailie J. E. Gallant, and Nigel J. Tapper. "The Sensitivity of Urban Meteorology to Soil Moisture Boundary Conditions: A Case Study in Melbourne, Australia." Journal of Applied Meteorology and Climatology 56, no. 8 (August 2017): 2155–72. http://dx.doi.org/10.1175/jamc-d-17-0007.1.
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AbstractThe sensitivity of near-surface urban meteorological conditions to three different soil moisture initialization experiments under heat-wave conditions is investigated for the city of Melbourne, Australia. The Weather Research and Forecasting Model is used to simulate a domain over Melbourne and its surrounding rural areas. The experiments employ three suites of simulations. Two suites initialize the model with soil moisture from the top layer of the ERA-Interim soil moisture data with a 3-month and 24-h coupled spinup period, respectively. The third suite initializes the model with the arguably more realistic soil moistures from the Australian Water Availability Project (AWAP), which are an order of magnitude drier than the ERA-Interim data, again using a 24-h spinup period. The simulations employing the AWAP data are found to have smaller errors when compared with observations, with biases in urban maximum temperature reduced by 4.1°C and biases in the skin temperature reduced by 3.0°C relative to the biases of the 3-month-spinup experiment. Despite urban areas only having a small proportion of soil-covered surfaces, the results show that urban soils have a greater influence on urban near-surface temperatures at night, whereas rural soils have a greater influence on urban near-surface temperatures during the daytime.
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Smith, R., and M. Reeder. "A review of research on the dry season mesoscale meteorology of northern Australia." Australian Meteorological and Oceanographic Journal 64, no. 1 (March 2014): S9—S43. http://dx.doi.org/10.22499/2.6401.008.
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Tomašević, Ivana Čavlina, Kevin K. W. Cheung, Višnjica Vučetić, and Paul Fox-Hughes. "Comparison of Wildfire Meteorology and Climate at the Adriatic Coast and Southeast Australia." Atmosphere 13, no. 5 (May 7, 2022): 755. http://dx.doi.org/10.3390/atmos13050755.
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Wildfire is one of the most complex natural hazards. Its origin is a combination of anthropogenic factors, urban development and weather plus climate factors. In particular, weather and climate factors possess many spatiotemporal scales and various degrees of predictability. Due to the complex synergy of the human and natural factors behind the events, every wildfire is unique. However, there are indeed common meteorological and climate factors leading to the high fire risk before certain ignition mechanismfigures occur. From a scientific point of view, a better understanding of the meteorological and climate drivers of wildfire in every region would enable more effective seasonal to annual outlook of fire risk, and in the long term, better applications of climate projections to estimate future scenarios of wildfire. This review has performed a comparison study of two fire-prone regions: southeast Australia including Tasmania, and the Adriatic coast in Europe, especially events in Croatia. The former is well known as part of the ‘fire continent’, and major resources have been put into wildfire research and forecasting. The Adriatic coast is a region where some of the highest surface wind speeds, under strong topographic effect, have been recorded and, over the years, have coincided with wildfire ignitions. Similar synoptic background and dynamic origins of the meso-micro-scale meteorological conditions of these high wind events as well as the accompanied dryness have been identified between some of the events in the two regions. We have also reviewed how the researchers from these two regions have applied different weather indices and numerical models. The status of estimating fire potential under climate change for both regions has been evaluated. This review aims to promote a global network of information exchange to study the changing anthropogenic and natural factors we have to confront in order to mitigate and adapt the impacts and consequences from wildfire.
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Su, Chun-Hsu, Nathan Eizenberg, Peter Steinle, Dörte Jakob, Paul Fox-Hughes, Christopher J. White, Susan Rennie, Charmaine Franklin, Imtiaz Dharssi, and Hongyan Zhu. "BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia." Geoscientific Model Development 12, no. 5 (May 24, 2019): 2049–68. http://dx.doi.org/10.5194/gmd-12-2049-2019.
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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 (<2 km) models.
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Amirthanathan, Gnanathikkam Emmanuel, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema. "Regional significance of historical trends and step changes in Australian streamflow." Hydrology and Earth System Sciences 27, no. 1 (January 11, 2023): 229–54. http://dx.doi.org/10.5194/hess-27-229-2023.
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Abstract. The Hydrologic Reference Stations is a network of 467 high-quality streamflow gauging stations across Australia that is developed and maintained by the Bureau of Meteorology as part of an ongoing responsibility under the Water Act 2007. The main objectives of the service are to observe and detect climate-driven changes in observed streamflow and to provide a quality-controlled dataset for research. We investigate trends and step changes in streamflow across Australia in data from all 467 streamflow gauging stations. Data from 30 to 69 years in duration ending in February 2019 were examined. We analysed data in terms of water-year totals and for the four seasons. The commencement of the water year varies across the country – mainly from February–March in the south to September–October in the north. We summarized our findings for each of the 12 drainage divisions defined by Australian Hydrological Geospatial Fabric (Geofabric) and for continental Australia as a whole. We used statistical tests to detect and analyse linear and step changes in seasonal and annual streamflow. Monotonic trends were detected using modified Mann–Kendall (MK) tests, including a variance correction approach (MK3), a block bootstrap approach (MK3bs) and a long-term persistence approach (MK4). A nonparametric Pettitt test was used for step-change detection and identification. The regional significance of these changes at the drainage division scale was analysed and synthesized using a Walker test. The Murray–Darling Basin, home to Australia's largest river system, showed statistically significant decreasing trends for the region with respect to the annual total and all four seasons. Drainage divisions in New South Wales, Victoria and Tasmania showed significant annual and seasonal decreasing trends. Similar results were found in south-western Western Australia, South Australia and north-eastern Queensland. There was no significant spatial pattern observed in central nor mid-west Western Australia, with one possible explanation for this being the sparse density of streamflow stations and/or the length of the datasets available. Only the Tanami–Timor Sea Coast drainage division in northern Australia showed increasing trends and step changes in annual and seasonal streamflow that were regionally significant. Most of the step changes occurred during 1970–1999. In the south-eastern part of Australia, the majority of the step changes occurred in the 1990s, before the onset of the “Millennium Drought”. Long-term monotonic trends in observed streamflow and its regional significance are consistent with observed changes in climate experienced across Australia. The findings of this study will assist water managers with long-term infrastructure planning and management of water resources under climate variability and change across Australia.
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Garratt, J. R., E. K. Webb, and S. McCarthy. "Charles Henry Brian Priestley 1915 - 1998." Historical Records of Australian Science 22, no. 1 (2011): 126. http://dx.doi.org/10.1071/hr11001.
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Charles Henry Brian Priestley (known as Bill) was born and educated in England. After completing the Mathematical Tripos at the University of Cambridge, he joined the Meteorological Office in 1939. In 1946, aged 31 years, he took up an Australian appointment with the Council for Scientific and Industrial Research (CSIR, later to become CSIRO) to establish and develop a group to undertake research in meteorological physics. Thereafter he was based in Melbourne, Australia. The group earned world recognition, particularly for its investigations of turbulent transfer in the lower atmosphere, and evolved to become the CSIRO Division of Atmospheric Research. Priestley's own early research focused on large-scale atmospheric systems, including substantial work on global-scale transport, and later on small-scale atmospheric convection and heat transfer, in which he established some significant results. He had a leading role in the development of the atmospheric sciences in Australia, and was strongly involved in international meteorology. His career with CSIRO extended to 1977, and he finally retired from all professional commitments in the mid-1980s. After several years of declining health, he died on 18 May 1998, seven weeks before he turned 83.
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Haidar, Ali, and Brijesh Verma. "Monthly rainfall forecasting using neural networks for sugarcane regions in Eastern Australia." Water Supply 17, no. 4 (June 20, 2016): 907–20. http://dx.doi.org/10.2166/ws.2016.099.
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Sugarcane is an important agricultural crop grown on the east coast of Australia. The timing and amount of rainfall is critical in determining both the yield of sugar and scheduling of harvesting operations. Rainfall forecasts issued through the Australian Bureau of Meteorology are based on general circulation models (GCMs) and have a poor skill levels. They are also limited in utility to end-users such as farmers as they cover very broad geographical areas and are only issued as probabilities above or below median. This paper presents an alternative approach for forecasting monthly rainfall with up to 12 month lead-time based on machine learning, in particular neural networks. Monthly rainfall forecasts have been developed for the eight locations in Eastern Australia at 3 and 12 month lead-time. The accuracy of the forecasts has been assessed relative to a skill scale with 0% representing climatology (the long term average) and 100% representing a perfect forecast (observation). On this scale, neural network forecasts are typically in the range 39.9–68% for all months using a single month optimization. This compares very favorably with forecasts using GCM from the Bureau that have skill levels only in the range −20% to 20%.
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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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Tozer, C. R., A. S. Kiem, and D. C. Verdon-Kidd. "On the uncertainties associated with using gridded rainfall data as a proxy for observed." Hydrology and Earth System Sciences Discussions 8, no. 5 (September 15, 2011): 8399–433. http://dx.doi.org/10.5194/hessd-8-8399-2011.
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Abstract. Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods)? This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets – the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia (SA) initially using gridded data as the source of rainfall input and then gauged rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged or point data. Rather the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.
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Fu, Guobin, Neil R. Viney, Stephen P. Charles, and Jianrong Liu. "Long-Term Temporal Variation of Extreme Rainfall Events in Australia: 1910–2006." Journal of Hydrometeorology 11, no. 4 (August 1, 2010): 950–65. http://dx.doi.org/10.1175/2010jhm1204.1.
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Abstract The temporal variability of the frequency of short-duration extreme precipitation events in Australia for the period 1910–2006 is examined using the high-quality rainfall dataset identified by the Bureau of Meteorology, Australia, for 189 stations. Extreme events are defined by duration and recurrence interval: 1, 5, 10, and 30 days, and 1, 5, and 20 yr, respectively. The results indicate that temporal variations of the extreme precipitation index (EPI) for various durations and recurrence intervals in the last 100 yr, except for the low frequencies before 1918, have experienced three U-shaped cycles: 1918–53, 1953–74, and 1974–2006. Seasonal results indicate that about two-thirds of 1-day, 1-yr recurrence interval extreme events occur from December to March. Time series of anomalies of the regional EPIs for four regions indicate that northeast Australia and southeast Australia have almost the same temporal variation as the national anomalies, South Australia experienced a negative anomaly of extreme rainfall events in the mid-1950s, and southwest Western Australia (SWWA) experienced relatively small temporal variation. The relationships between extreme rainfall events and the Southern Oscillation index (SOI) and the interdecadal Pacific oscillation (IPO) indicate that extreme rainfall events in Australia have a strong relationship with both, especially during La Niña years and after 1942.
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Hall, Lisa S., Meredith L. Orr, Megan E. Lech, Steven Lewis, Adam H. E. Bailey, Ryan Owens, Barry E. Bradshaw, and George Bernardel. "Geological and Bioregional Assessments: assessing the prospectivity for tight, shale and deep-coal resources in the Cooper Basin, Beetaloo Subbasin and Isa Superbasin." APPEA Journal 61, no. 2 (2021): 477. http://dx.doi.org/10.1071/aj20035.
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The Geological and Bioregional Assessment Program is a series of independent scientific studies undertaken by Geoscience Australia and the CSIRO, supported by the Bureau of Meteorology, and managed by the Department of Agriculture, Water and the Environment. The program consists of three stages across three regions with potential to deliver gas to the East Coast Gas Market. Stage 1 was a rapid regional prioritisation conducted by Geoscience Australia, to identify those sedimentary basins with the greatest potential to deliver shale and/or tight gas to the East Coast Gas Market within the next 5–10 years. This prioritisation process assessed 27 onshore eastern and northern Australian basins with shale and/or tight gas potential. Further screening reduced this to a shortlist of nine basins where exploration was underway. The shortlisted basins were ranked on a number of criteria. The Cooper Basin, the Beetaloo Subbasin and the Isa Superbasin were selected for more detailed assessment. Stage 2 of the program involved establishing a baseline understanding of the identified regions. Geoscience Australia produced regional geological evaluations and conceptualisations that informed the assessment of shale and/or tight gas prospectivity, ground- and surface-water impacts and hydraulic fracturing models. Geoscience Australia’s relative prospectivity assessments provide an indication of where viable petroleum plays are most likely to be present. These data indicate areal and stratigraphic constraints that support the program’s further work in Stage 3, on understanding likely development scenarios, impact assessments and causal pathways.
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Peace, Mika, Trent Mattner, Graham Mills, Jeffrey Kepert, and Lachlan McCaw. "Fire-Modified Meteorology in a Coupled Fire–Atmosphere Model." Journal of Applied Meteorology and Climatology 54, no. 3 (March 2015): 704–20. http://dx.doi.org/10.1175/jamc-d-14-0063.1.
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AbstractThe coupled fire–atmosphere model consisting of the Weather and Forecasting (WRF) Model coupled with the fire-spread model (SFIRE) module has been used to simulate a bushfire at D’Estrees Bay on Kangaroo Island, South Australia, in December 2007. Initial conditions for the simulations were provided by two global analyses: the GFS operational analysis and ERA-Interim. For each NWP initialization, the simulations were run with and without feedback from the fire to the atmospheric model. The focus of this study was examining how the energy fluxes from the simulated fire modified the local meteorological environment. With feedback enabled, the propagation speed of the sea-breeze frontal line was faster and vertical motion in the frontal zone was enhanced. For one of the initial conditions with feedback on, a vortex developed adjacent to the head fire and remained present for over 5 h of simulation time. The vortex was not present without fire–atmosphere feedback. The results show that the energy fluxes released by a fire can effect significant changes on the surrounding mesoscale atmosphere. This has implications for the appropriate use of weather parameters extracted from NWP and used in prediction for fire operations. These meteorological modifications also have implications for anticipating likely fire behavior.
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Manage, Nadeeka Parana, Natalie Lockart, Garry Willgoose, George Kuczera, Anthony S. Kiem, AFM Kamal Chowdhury, Lanying Zhang, and Callum Twomey. "Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia." Journal of Southern Hemisphere Earth Systems Science 66, no. 2 (2016): 203. http://dx.doi.org/10.1071/es16016.
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This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale.
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Monk, Khalia, Elise-Andrée Guérette, Clare Paton-Walsh, Jeremy D. Silver, Kathryn M. Emmerson, Steven R. Utembe, Yang Zhang, et al. "Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison." Atmosphere 10, no. 7 (July 4, 2019): 374. http://dx.doi.org/10.3390/atmos10070374.
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The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s−1 mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons.
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Islam, Mohammad S., Tianxin Fang, Callum Oldfield, Puchanee Larpruenrudee, Hamidreza Mortazavy Beni, Md M. Rahman, Shahid Husain, and Yuantong Gu. "Heat Wave and Bushfire Meteorology in New South Wales, Australia: Air Quality and Health Impacts." International Journal of Environmental Research and Public Health 19, no. 16 (August 20, 2022): 10388. http://dx.doi.org/10.3390/ijerph191610388.
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The depletion of air quality is a major problem that is faced around the globe. In Australia, the pollutants emitted by bushfires play an important role in making the air polluted. These pollutants in the air result in many adverse impacts on the environment. This paper analysed the air pollution from the bushfires from November 2019 to July 2020 and identified how it affects the human respiratory system. The bush fires burnt over 13 million hectares, destroying over 2400 buildings. While these immediate effects were devastating, the long-term effects were just as devastating, with air pollution causing thousands of people to be admitted to hospitals and emergency departments because of respiratory complications. The pollutant that caused most of the health effects throughout Australia was Particulate Matter (PM) PM2.5 and PM10. Data collection and analysis were covered in this paper to illustrate where and when PM2.5 and PM10, and other pollutants were at their most concerning levels. Susceptible areas were identified by analysing environmental factors such as temperature and wind speed. The study identified how these pollutants in the air vary from region to region in the same time interval. This study also focused on how these pollutant distributions vary according to the temperature, which helps to determine the relationship between the heatwave and air quality. A computational model for PM2.5 aerosol transport to the realistic airways was also developed to understand the bushfire exhaust aerosol transport and deposition in airways. This study would improve the knowledge of the heat wave and bushfire meteorology and corresponding respiratory health impacts.
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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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Brunner, D., P. Siegmund, P. T. May, L. Chappel, C. Schiller, R. Müller, T. Peter, et al. "The SCOUT-O3 Darwin Aircraft Campaign: rationale and meteorology." Atmospheric Chemistry and Physics Discussions 8, no. 5 (September 11, 2008): 17131–91. http://dx.doi.org/10.5194/acpd-8-17131-2008.
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Abstract. An aircraft measurement campaign involving the Russian high-altitude aircraft M55 Geophysica and the German DLR Falcon was conducted in Darwin, Australia in November and December 2005 as part of the European integrated project SCOUT-O3. The overall objectives of the campaign were to study the transport of trace gases through the tropical tropopause layer (TTL), mechanisms of dehydration close to the tropopause, and the role of deep convection in these processes. In this paper a detailed roadmap of the campaign is presented, including rationales for each flight, and an analysis of the local and large-scale meteorological context in which they were embedded. The campaign took place during the pre-monsoon season which is characterized by a pronounced diurnal evolution of deep convection including a mesoscale system over the Tiwi Islands north of Darwin known as "Hector". This allowed studying in detail the role of deep convection in structuring the tropical tropopause region, in situ sampling convective overshoots above storm anvils, and probing the structure of anvils and cirrus clouds by Lidar and a suite of in situ instruments onboard the two aircraft. The large-scale flow during the first half of the campaign was such that local flights, away from convection, sampled air masses downstream of the "cold trap" region over Indonesia. Abundant cirrus clouds enabled the study of active dehydration, in particular during two TTL survey flights. The campaign period also encompassed a Rossby wave breaking event transporting stratospheric air to the tropical middle troposphere and an equatorial Kelvin wave modulating tropopause temperatures and hence the conditions for dehydration.
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Brunner, D., P. Siegmund, P. T. May, L. Chappel, C. Schiller, R. Müller, T. Peter, et al. "The SCOUT-O3 Darwin Aircraft Campaign: rationale and meteorology." Atmospheric Chemistry and Physics 9, no. 1 (January 8, 2009): 93–117. http://dx.doi.org/10.5194/acp-9-93-2009.
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Abstract. An aircraft measurement campaign involving the Russian high-altitude aircraft M55 Geophysica and the German DLR Falcon was conducted in Darwin, Australia in November and December 2005 as part of the European integrated project SCOUT-O3. The overall objectives of the campaign were to study the transport of trace gases through the tropical tropopause layer (TTL), mechanisms of dehydration close to the tropopause, and the role of deep convection in these processes. In this paper a detailed roadmap of the campaign is presented, including rationales for each flight, and an analysis of the local and large-scale meteorological context in which they were embedded. The campaign took place during the pre-monsoon season which is characterized by a pronounced diurnal evolution of deep convection including a mesoscale system over the Tiwi Islands north of Darwin known as "Hector". This allowed studying in detail the role of deep convection in structuring the tropical tropopause region, in situ sampling convective overshoots above storm anvils, and probing the structure of anvils and cirrus clouds by Lidar and a suite of in situ instruments onboard the two aircraft. The large-scale flow during the first half of the campaign was such that local flights, away from convection, sampled air masses downstream of the "cold trap" region over Indonesia. Abundant cirrus clouds enabled the study of active dehydration, in particular during two TTL survey flights. The campaign period also encompassed a Rossby wave breaking event transporting stratospheric air to the tropical middle troposphere and an equatorial Kelvin wave modulating tropopause temperatures and hence the conditions for dehydration.
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Sharma, Saroj Kumar, Jagannath Aryal, and Abbas Rajabifard. "Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia." Remote Sensing 14, no. 7 (March 29, 2022): 1645. http://dx.doi.org/10.3390/rs14071645.
Full textAbstract:
The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts.
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Ullah, Fahim, Sara Imran Khan, Hafiz Suliman Munawar, Zakria Qadir, and Siddra Qayyum. "UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires." Sustainability 13, no. 18 (September 13, 2021): 10207. http://dx.doi.org/10.3390/su131810207.
Full textAbstract:
Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.
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Power, Scott, Malcolm Haylock, Rob Colman, and Xiangdong Wang. "The Predictability of Interdecadal Changes in ENSO Activity and ENSO Teleconnections." Journal of Climate 19, no. 19 (October 1, 2006): 4755–71. http://dx.doi.org/10.1175/jcli3868.1.
Full textAbstract:
Abstract El Niño–Southern Oscillation (ENSO) in a century-long integration of a Bureau of Meteorology Research Centre (BMRC) coupled general circulation model (CGCM) drives rainfall and temperature changes over Australia that are generally consistent with documented observational changes: dry/hot conditions occur more frequently during El Niño years and wet/mild conditions occur more frequently during La Niña years. The relationship between ENSO [as measured by Niño-4 or the Southern Oscillation index (SOI), say] and all-Australia rainfall and temperature is found to be nonlinear in the observations and in the CGCM during June–December: a large La Niña sea surface temperature (SST) anomaly is closely linked to a large Australian response (i.e., Australia usually becomes much wetter), whereas the magnitude of an El Niño SST anomaly is a poorer guide to how dry Australia will actually become. Australia tends to dry out during El Niño events, but the degree of drying is not as tightly linked to the magnitude of the El Niño SST anomaly. Nonlinear or asymmetric teleconnections are also evident in the western United States/northern Mexico. The implications of asymmetric teleconnections for prediction services are discussed. The relationship between ENSO and Australian climate in both the model and the observations is strong in some decades, but weak in others. A series of decadal-long perturbation experiments are used to show that if these interdecadal changes are predictable, then the level of predictability is low. The model’s Interdecadal Pacific Oscillation (IPO), which represents interdecadal ENSO-like SST variability, is statistically linked to interdecadal changes in ENSO’s impact on Australia during June–December when ENSO’s impact on Australia is generally greatest. A simple stochastic model that incorporates the nonlinearity above is used to show that the IPO [or the closely related Pacific Decadal Oscillation (PDO)] can appear to modulate ENSO teleconnections even if the IPO–PDO largely reflect unpredictable random changes in, for example, the relative frequency of El Niño and La Niña events in a given interdecadal period. Note, however, that predictability in ENSO-related variability on decadal time scales might be either underestimated by the CGCM, or be too small to be detected by the modest number of perturbation experiments conducted. If there is a small amount of predictability in ENSO indices on decadal time scales, and there may be, then the nonlinearity described above provides a mechanism via which ENSO teleconnections could be modulated on decadal time scales in a partially predictable fashion.
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Tozer, C. R., A. S. Kiem, and D. C. Verdon-Kidd. "On the uncertainties associated with using gridded rainfall data as a proxy for observed." Hydrology and Earth System Sciences 16, no. 5 (May 23, 2012): 1481–99. http://dx.doi.org/10.5194/hess-16-1481-2012.
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Abstract. Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets – the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids – particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.
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Feng, Ming, Yongliang Duan, Susan Wijffels, Je-Yuan Hsu, Chao Li, Huiwu Wang, Yang Yang, et al. "Tracking Air–Sea Exchange and Upper-Ocean Variability in the Indonesian–Australian Basin during the Onset of the 2018/19 Australian Summer Monsoon." Bulletin of the American Meteorological Society 101, no. 8 (August 1, 2020): E1397—E1412. http://dx.doi.org/10.1175/bams-d-19-0278.1.
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Abstract Sea surface temperatures (SSTs) north of Australia in the Indonesian–Australian Basin are significantly influenced by Madden–Julian oscillation (MJO), an eastward-moving atmospheric disturbance that traverses the globe in the tropics. The region also has large-amplitude diurnal SST variations, which may influence the air–sea heat and moisture fluxes, that provide feedback to the MJO evolution. During the 2018/19 austral summer, a field campaign aiming to better understand the influences of air–sea coupling on the MJO was conducted north of Australia in the Indonesian–Australian Basin. Surface meteorology from buoy observations and upper-ocean data from autonomous fast-profiling float observations were collected. Two MJO convective phases propagated eastward across the region in mid-December 2018 and late January 2019 and the second MJO was in conjunction with a tropical cyclone development. Observations showed that SST in the region was rather sensitive to the MJO forcing. Air–sea heat fluxes warmed the SST throughout the 2018/19 austral summer, punctuated by the MJO activities, with a 2°–3°C drop in SST during the two MJO events. Substantial diurnal SST variations during the suppressed phases of the MJOs were observed, and the near-surface thermal stratifications provided positive feedback for the peak diurnal SST amplitude, which may be a mechanism to influence the MJO evolution. Compared to traditionally vessel-based observation programs, we have relied on fast-profiling floats as the main vehicle in measuring the upper-ocean variability from diurnal to the MJO time scales, which may pave the way for using cost-effective technology in similar process studies.
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Alroe, Joel, Luke T. Cravigan, Branka Miljevic, Graham R. Johnson, Paul Selleck, Ruhi S. Humphries, Melita D. Keywood, Scott D. Chambers, Alastair G. Williams, and Zoran D. Ristovski. "Marine productivity and synoptic meteorology drive summer-time variability in Southern Ocean aerosols." Atmospheric Chemistry and Physics 20, no. 13 (July 10, 2020): 8047–62. http://dx.doi.org/10.5194/acp-20-8047-2020.
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Abstract. Cloud–radiation interactions over the Southern Ocean are not well constrained in climate models, in part due to uncertainties in the sources, concentrations, and cloud-forming potential of aerosol in this region. To date, most studies in this region have reported measurements from fixed terrestrial stations or a limited set of instrumentation and often present findings as broad seasonal or latitudinal trends. Here, we present an extensive set of aerosol and meteorological observations obtained during an austral summer cruise across the full width of the Southern Ocean south of Australia. Three episodes of continental-influenced air masses were identified, including an apparent transition between the Ferrel atmospheric cell and the polar cell at approximately 64∘ S, and accompanied by the highest median cloud condensation nuclei (CCN) concentrations, at 252 cm−3. During the other two episodes, synoptic-scale weather patterns diverted air masses across distances greater than 1000 km from the Australian and Antarctic coastlines, respectively, indicating that a large proportion of the Southern Ocean may be periodically influenced by continental air masses. In all three cases, a highly cloud-active accumulation mode dominated the size distribution, with up to 93 % of the total number concentration activating as CCN. Frequent cyclonic weather conditions were observed at high latitudes and the associated strong wind speeds led to predictions of high concentrations of sea spray aerosol. However, these modelled concentrations were not achieved due to increased aerosol scavenging rates from precipitation and convective transport into the free troposphere, which decoupled the air mass from the sea spray flux at the ocean surface. CCN concentrations were more strongly impacted by high concentrations of large-diameter Aitken mode aerosol in air masses which passed over regions of elevated marine biological productivity, potentially contributing up to 56 % of the cloud condensation nuclei concentration. Weather systems were vital for aerosol growth in biologically influenced air masses and in their absence ultrafine aerosol diameters were less than 30 nm. These results demonstrate that air mass meteorological history must be considered when modelling sea spray concentrations and highlight the potential importance of sub-grid-scale variability when modelling atmospheric conditions in the remote Southern Ocean.
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Horton, Brian, and Ross Corkrey. "A weighted coefficient model for estimation of Australian daily soil temperature at depths of 5cm to 100cm based on air temperature and rainfall." Soil Research 49, no. 4 (2011): 305. http://dx.doi.org/10.1071/sr10151.
Full textAbstract:
Soil temperatures are related to air temperature and rainfall on the current day and preceding days, and this can be expressed in a non-linear relationship to provide a weighted value for the effect of air temperature or rainfall based on days lag and soil depth. The weighted minimum and maximum air temperatures and weighted rainfall can then be combined with latitude and a seasonal function to estimate soil temperature at any depth in the range 5–100 cm. The model had a root mean square deviation of 1.21–1.85°C for minimum, average, and maximum soil temperature for all weather stations in Australia (mainland and Tasmania), except for maximum soil temperature at 5 and 10 cm, where the model was less precise (3.39° and 2.52°, respectively). Data for this analysis were obtained from 32–40 Bureau of Meteorology weather stations throughout Australia and the proposed model was validated using 5-fold cross-validation.