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  • This database contains the monthly mean and montly long term mean fields from the NCEP/NCAR Reanalysis 1960-2000. Files contain the following data: airsfc.mon.mean.nc - surface air temperature land.nc - land/sea mask slp.mon.mean.nc - sea level pressure sst.mnmean.nc - sea surface temperature (see SST_README for more details) uwnd.mon.mean.nc - U (eastward) component of wind vwnd.mon.mean.nc - V (northward) component of wind shum.mon.mean.nc - specific humidity (this file does not contain all vertical levels, unlike the other 3-d variables) For all the above, files with 'ltm' instead of 'mean' contain the long-term monthly mean data. Data were downloaded on 25/11/2009 from the Earth System Reseach Laboratory (ESRL) Physical Sciences Division (PSD) website. (http://www.esrl.noaa.gov/psd/data/gridded/reanalysis/)

  • Developing a framework and computational methodology for evaluating the impacts and risks of extreme fire events on regional and peri-urban populations (infrastructure and people) applicable to the Australian region. The research considers three case studies of recent extreme fires employing an ensemble approach (sensitivity analysis) which varies the meteorology, vegetation and ignition in an effort to estimate fire risk to the case-study fire area and adjacent region.

  • Results from the first pass application of the tomography technique using low accuracy sensors is presented and limitations of the sensors and technique discussed. BUll. Seismol. Soc. AM.

  • Modelling tropical cyclone Yasi using TCRM

  • The world's first satellite-derived mineral maps of a continent, namely Australia, are now publicly available as digital, web-accessible products. The value of this spatially comprehensive mineral information is readily being captured by explorers at terrane to prospect scales. However, potentially even greater benefits can ensue for environmental applications, especially for the Earth's extensive drylands which generate nearly 50% of the world's agricultural production but are most at risk to climate change and poor land management. Here we show how these satellite mineral maps can be used to: characterise soil types; define the extent of deserts; fingerprint sources of dust; measure the REDOX of iron minerals as a potential marine input; and monitor the process of desertification. We propose a 'Mineral Desertification Index' that can be applied to all Earth's drylands where the agriculturally productive clay mineral component is being lost by erosion. Mineral information is fundamental to understanding geology and is important for resource applications1. Minerals are also a fundamental component of soils2 as well as dust eroded from the land surface, which can potentially impact on human health3, the marine environment4 and climate5. Importantly, minerals are well exposed in the world's 'drylands', which account for nearly 50% of Earth's land area6. Here, vegetation cover is sparse to non-existent as a result of low rainfall (P) and high evaporation (E) rates (P/E<0.65). However, drylands support 50% of the world's livestock production and almost half of all cultivated systems6. In Australia, drylands cover 85% of the continent and account for 50% of its beef, 80% of its sheep and 93% of its grain production7. Like other parts of the world, Australia is facing serious desertification of its drylands6. Wind, overgrazing and overstocking are major factors in the desertification process8. That is, the agriculturally productive clay-size fraction of soils (often includes organic carbon) is lost largely through wind erosion, which is acerbated by the loss of any vegetative groundcover (typically dry plant materials). Once clay (and carbon) loss begins, then the related break down of the soil structure and loss of its water holding capacity increases the rate of the degeneration process with the final end products being either exposed rock or quartz sands that often concentrate in deserts.

  • Natural hazard data supports the nation to respond effectively to emergencies, reduce the threat natural hazards pose to Australia¿s national interests and address issues relating to community safety, urban development, building design, climate change and insurance. A baseline understanding of hazards, impacts and risk can help to enhance community resilience to extreme events and a changing environment. Probabilistic hazard and risk information provides planners and designers opportunity to investigate the cost and benefit of policy options to mitigate natural hazard impacts. Modelled disaster scenario information can enable disaggregation of probabilistic hazard to identify the most probable event contributing to hazard. Tropical cyclone return period wind hazard maps developed using the Tropical Cyclone Risk Model. The hazard maps are derived from a catalogue of synthetic tropical cyclone events representing 10,000 years of activity. Annual maxima are evaluated from the catalogue and used to fit a generalised extreme value distribution at each grid point. Wind multipliers are factors that transform regional wind speed to local wind speed, mathematically describing the influences of terrain, shielding and topographic effects. Local wind speeds are critical to wind-related activities that include hazard and risk assessment. The complete dataset is comprised of: - Stochastic tracks, wind fields and impact data; - Probabilistic wind speed data (hazard); - Site-exposure wind multipliers.

  • Weather radar data provided by the Bureau of Meteorology for initial investigation into thunderstorm tracking and analysis applications

  • Following the drilling of a shallow CO2 reservoir at the Qinghai research site, west of Haidong, China, it was discovered that CO2 was continuously leaking from the wellbore due to well-failure. The site has become a useful facility in China for studying CO2 leakage and monitoring technologies for application to geological storage sites of CO2. During an eight day period in 2014, soil gas and soil flux surveys were conducted to characterise the distribution, magnitude and likely source of the leaking CO2. Two different sampling patterns were utilised during soil flux surveys. A regular sampling grid was used to spatially map out the two high flux zones which were located 20-50 m away from the wellhead. An irregular sampling grid with higher sampling density in the high flux zones, allowed for more accurate mapping of the leak distribution and estimation of total field emission rate using cubic interpolation. The total CO2 emission rate for the site was estimated at 649-1015 kgCO2/d and there appeared to be some degree of spatial correlation between observed CO2 fluxes and elevated surface H2O fluxes. Sixteen soil gas wells were installed across the field to test the real-time application of Romanak et al.'s (2012) process-based approach for soil gas measurements (using ratios of major soil gas components to identify the CO2 source) using a portable multi-gas analyser. Results clearly identified CO2 as being derived from one exogenous source, and are consistent with gas samples collected for laboratory analysis. Carbon-13 isotopes in the centre of each leak zone (-0.21 and -0.22 ) indicate the underlying CO2 is likely sourced from the thermal decomposition of marine carbonates. Surface soil mineralisation (predominantly calcite) is used to infer prior distribution of the CO2 hotspots and as a consequence highlighted plume migration of 20 m in 11 years. Detachment of the plume from the wellbore at the Qinghai research site markedly increases the area that needs surveying at sufficient density to detect a leak. This challenges the role of soil flux and soil gas in a CCS monitoring and verification program for leak detection, whereas these techniques may best be applied for characterising source and emission rate of a CO2 leak.

  • Source The data was sourced from CSIRO (Victoria) in 2012 by Bob Cechet. It is not known specifically which division of CSIRO, although it is likely to have been the Marine and Atmospheric Research Division (Aspendale), nor the contact details of the person who provided the data to Bob. The data was originally produced by CSIRO for their input into the South-East Queensland Climate Adaptation Research Initiative (SEQCARI). Reference, from an email of 16 March 2012 sent from Bob Cechet to Chris Thomas (Appendix 1 of the README doc stored at the parent folder level with the data), is made to 'download NCEP AVN/GFS files' or to source them from the CSIRO archive. Content The data is compressed into 'tar' files. The name content is separated by a dot where the first section is the climatic variable as outlined in the table format below: Name Translation rain 24 hr accumulated precipitation rh1_3PM Relative humidity at 3pm local time tmax Maximum temperature tmin Minimum temperature tscr_3PM Screen temperature (2 m above ground) at 3pm local time u10_3PM 10-metre above ground eastward wind speed at 3pm local time v10_3PM 10-metre above ground northward wind speed at 3pm local time The second part of the name is the General Circulation Model (GCM) applied: Name Translation gfdlcm21 GFDL CM2.1 miroc3_2_medres MIROC 3.2 (medres) mpi_echam5 MPI ECHAM5 ncep NCEP The third, and final, part of the tarball name is the year range that the results relate to: 1961-2000, 1971-2000, 2001-2040 and 2041-2099 Data format and extent Inside each of the tarball files is a collection of NetCDF files covering each simulation that constitutes the year range (12 simulations for each year). A similar naming protocol is used for the NetCDF files with a two digit extension added to the year for each of the simulations for that year (e.g 01-12). The spatial coverage of the NetCDF files is shown in the bounding box extents as shown below. Max X: -9.92459297180176 Min X: -50.0749073028564 Max Y: 155.149784088135 Min Y: 134.924812316895 The cell size is 0.15 degrees by 0.15 degrees (approximately 17 km square at the equator) The data is stored relative to the WGS 1984 Geographic Coordinate System. The GCMs were forced with the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario as described in the IPCC Special Report on Emissions Scenarios (SRES) inputs for the future climate. The GCM results were then downscaled from a 2 degree cell resolution by CSIRO using their Cubic Conformal Atmospheric Model (CCAM) to the 0.15 degree cell resolution. Use This data was used within the Rockhampton Project to identify the future climate changes based on the IPCC A2 SRES emissions scenario. The relative difference of the current climate GCM results to the future climate results was applied to the results of higher resolution current climate natural hazard modelling. Refer to GeoCat # 75085 for the details relating to the report and the 59 attached ANZLIC metadata entries for data outputs.

  • The Bushfire Attack Level Toolbox provides access to ArcGIS geoprocessing scripts that calculate the Bushfire Attack Level (BAL) as per Method 1 in AS-3959 (2009). BAL is a measure of the severity of a building's potential exposure to ember attack, radiant heat and direct flame contact in the event of a bushfire. It serves as a basis for establishing the requirements for construction to improve protection of building elements from attack by bushfire. The BAL Toolbox User GUIDE provides users an overvoew of the Toolbox, instructions on installation, any customisations, execution and evaluation of results.