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  • Eddy Covariance (EC) is considered a key atmospheric technique for quantifying CO2 leakage. However the complex and localised heterogeneity of a CO2 leak above the background environmental signal violates several of the critical assumptions made when implementing the EC technique, including: - That horizontal gradients in CO2 concentration are zero. - That horizontal and vertical gradients in the covariance of CO2 and orthogonal wind directions are zero. The ability of EC measurements of CO2 flux at the surface to provide information on the location and strength of CO2 leakage from below ground stores was tested during a 144 kg/day release event (27 March - 13 June 2012) at the Ginninderra controlled release facility. We show that the direction of the leak can be ascertained with some confidence although this depends on leak strength and distance from leak. Elevated CO2 levels are seen in the direction of the leakage area, however quantifying the emissions is confounded by the potential bias within each measurement through breaching of the assumptions underpinning the EC technique. The CO2 flux due to advection of the horizontal CO2 concentration gradients, thought to be the largest component of the error with the violation of the EC technique's assumptions, has been estimated using the modelling software Windtrax. The magnitude of the CO2 flux due to advection is then compared with the measured CO2 flux measured using the EC technique, to provide an initial assessment of the suitability of the EC technique to quantifying leakage source rates.

  • 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

  • This wind field was produced within v2.0 of TCRM, using data from the Bureau of Meteorology to constrain the wind field. Wind multipliers were calculated using a landcover dataset derived from Landsat and Digital Earth Australia. This wind field may be refined in the future as new data becomes available. This record includes - the track data from the Bureau of Meteorology used to model Tropical Cyclone Debbie - the landcover dataset produced for the Airlie Beach region - the preliminary local wind field

  • Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day. Typical values for daily global exposure range from 1 to 35 MJ/m2 (megajoules per square metre). For mid-latitudes, the values are usually highest in clear sun conditions during the summer, and lowest during winter or very cloudy days. The monthly means are derived from the daily global solar exposure. See metadata statement for more information.

  • Included fields: Record identifier - hm Bureau of Meteorology Station Number. Year Month Day Hours Minutes in YYYY,MM,DD,HH24,MI format in Local time Year Month Day Hours Minutes in YYYY,MM,DD,HH24,MI format in Local standard time Air Temperature in degrees C Quality of Air Temperature Wet bulb temperature in degrees C Quality of Wet Bulb Temperature Dew point temperature in degrees C Quality of Dew point Temperature Relative humidity in percentage % Quality of Relative humidity Wind speed in km/h Quality of Wind speed Wind direction in degrees Quality of Wind direction Speed of maximum wind gust in last 10 minutes in km/h Quality of speed of maximum wind gust in last 10 minutes Automatic Weather Station Flag

  • Modelling tropical cyclone Yasi using TCRM

  • We have developed an autonomous CO2 monitoring station, based around the Vaisala GMP343 sensor. The station is powered by a solar panel and incorporates a data logger and a directional antenna for line-of-sight wireless communication with a base station. The base station communicates via the Telstra mobile phone data network. The concept of atmospheric tomography was tested at the Ginninderra site and proved very successful as a method of locating and quantifying a spatially small release of CO2. In this case the sensors were separated from the source by 40 m. The opportunity to test the method over a larger distance arose during the controlled release of Buttress gas during the stage 2B experiment at the Otway site. Gas was released at 8 tpd during daylight hours, and an approximate ring of 8 monitoring stations was deployed around the release point; the ring was about 800 m in diameter. Gas was released on 12 occasions, chosen to match wind directions that would carry the plume in the direction of one of the sensors. The dataset was too limited to carry out the full-scale Bayesian inversion that was demonstrated in the Ginninderra test (which lasted two months) but a simple inversion was possible. This located the source of the release correctly to within 20 m. The test demonstrated that inexpensive sensors could achieve enough stability and sensitivity to work (in this particular application) at the few ppm level. Moreover quite simple dispersion models could be used to predict plume geometry up to 500 m from the release. Overall the experiment indicates the basis of an inexpensive method for remotely monitoring areas of around a km2 for spatially small leakages.