There is increasing recognition that minimising methane emissions from the oil and gas sector is a key step in reducing global greenhouse gas emissions in the near term. Atmospheric monitoring techniques are likely to play an important future role in measuring the extent of existing emissions and verifying emission reductions. Geoscience Australia and CSIRO Marine & Atmospheric Research have collected three years of continuous methane and carbon dioxide measurements at their atmospheric composition monitoring station ('Arcturus') in the Bowen Basin, Australia. Methane signals in the Bowen Basin are likely to be influenced by cattle production, landfill, coal production, and conventional and coal seam gas (CSG) production. Australian CSG is typically 'dry' and is characterised by a mixed thermogenic-biogenic methane source with an absence of C3-C6+ alkanes. The range of ?13C isotopic signatures of the CSG is similar to methane from landfill gas and cattle emissions. The absence of standard in-situ tracers for CSG fugitive emissions suggests that having a comprehensive baseline will be critical for successful measurement of fugitive emissions using atmospheric techniques. In this paper we report on the sensitivity of atmospheric techniques for the detection of fugitive emissions for a simulated new CSG field against a three year baseline signal. Simulation of emissions was performed for a 1-year period using the coupled prognostic meteorological and air pollution model TAPM at different fugitive emission rates (i.e. 0.1 - 10 %) and distances (i.e. 10 - 50 km) from the station. Emissions from the simulated CSG field are based on well density, production volumes, and field size typical of CSG fields in Australia. The distributions of the perturbed and baseline signals were evaluated and statistically compared to test for the presence of fugitive methane emissions. In addition, a time series model of the methane baseline was developed in order to generate alternative realizations of the baseline signal. These were used to provide measures of both the likelihood of detecting fugitive emissions at various emission levels and of the false alarm rate. Results of statistical analysis and an indicative minimum fugitive methane emission rate that can be detected using a single monitoring station are presented. Submitted to AGU 2013, San Francisco

<p>Geoscience Australia in collaboration with the CO2CRC hosted three controlled subsurface release experiments of CO2 during 2012 to 2013 at an agricultural research station managed by CSIRO Plant Industry Canberra. The facility was designed to simulate surface emissions of CO2 and other greenhouse gases from the soil into the atmosphere, and has deployed a range of near-surface monitoring techniques in the pursuit of improving detection and quantification methods and technologies. This product, which encompasses 4 geodatabases, a metadata report and a data dictionary, presents all the data collected during the experiments from over 10 research organisations, and is made to use with GIS software. The intention of this data release is make the data available for comparison with measurements taken at other controlled release experiments, CO2 storage projects and natural analogues. This will hopefully facilitate the further development of greenhouse gas monitoring technologies, methods and monitoring strategies and increase our understanding of the migration behaviour and impact of near surface CO2 leakage. <p>The contents of each geodatabase/experiment is summarised below: <p>Release 1 (Feb-May 2012): <p>- Soil microbial data <p>- Soil chemistry <p>- Free air CO2 concentration <p>- Eddy covariance <p>- Groundwater chemistry <p>- Soil gas <p>- Krypton tracers <p>- EM31 <p>- Soil flux <p>Release 2 (Oct-Dec 2012): <p>- Groundwater chemistry <p>- EM31 <p>- EM38 <p>- Soil gas <p>- Soil flux <p>- Airborne hyperspectral <p>- Ground hyperspectral <p>Release 3 (Oct-Dec 2013): <p>- Mobile CO2 surveys <p>- Groundwater depth <p>- Eddy covariance <p>- Plant physiology and chemistry <p>- EM31 <p>- EM38 <p>- Soil gas <p>- Soil flux <p>- Airborne hyperspectral <p>All Releases: <p>- Aerial images <p>- Groundwater depths <p>- Meteorological data <p>Bibliographic reference: <p>Feitz, A.J., Schroder, I.F., Jenkins, C.J., Schacht, U., Zegelin, S., Berko, H., McGrath, A., Noble, R., Palu, T.J., George, S., Heath, C., Zhang, H., Sirault, X. and Jimenez-Berni, J. 2016. Ginninderra Controlled CO2 Release Facility Dataset 2012-2013. eCat 90078, Geoscience Australia and CO2CRC, Canberra. https://pid.geoscience.gov.au/dataset/ga/90078. <p>Digital Object Identifier: http://dx.doi.org/10.4225/25/5823c37333f9d

Tropical cyclones present a tangible risk to Australia’s tropical coastal communities, however extratropical transition (ETT) of these storms can result in significant impacts in mid-latitude regions as well. Tropical systems are driven by latent heat release in the inner core of the cyclone. A fully tropical system is highly axisymmetric; with a warm-cored vortex that is readily represented by a simple radial profile (wind speed is a function of distance from the centre in all directions). Extratropical cyclones on the other hand are driven by strong thermal gradients and as a result have a highly asymmetric wind field that cannot be as easily parameterised for use in stochastic models. In order to accurately model the risk of these transitioning storms on communities such as Perth, the wind field of these storms needs to be parameterised for inclusion in stochastic models. These models allow large numbers of storms to be quickly simulated for use in risk modelling applications. Some authors have attempted to develop parameterisations of these wind fields, with some recent success (Loridan et al. 2015), however an implementation for the Australian region has not yet been developed. Geoscience Australia currently undertakes tropical cyclone risk assessments using a parameterised, 2D stochastic model called the Tropical Cyclone Risk Model (TCRM). TCRM uses parameterised wind fields to allow quick generation of thousands of tropical cyclones in order to develop a probabilistic understanding of tropical cyclone risk for Australia. At present, this model is not capable of simulating tropical cyclones undergoing ETT as a parameterisation of the wind field of these storms around Australia is not available. This work aims to explore ETT around Australia using a 3D, dynamical numerical weather prediction model with the ultimate goal of developing a parameterised wind field, suitable for inclusion in TCRM. This would allow risk assessments for these storms to be undertaken, and improve our understanding of the potential impact of such an event on large urban areas, such as Geraldton or Perth. A modified version of the Weather Research and Forecast (WRF) model (Hybrid WRF) was used to simulate a number of hybrid idealised tropical cyclones, and steer them to undergo ETT. Hybrid WRF was developed to facilitate control over the track and location of landfall of a tropical cyclone, by introducing a steering flow to the boundary conditions of the model run. This method was used to steer a number of idealised tropical cyclones from off the northwest coast of Western Australia, south towards Perth, with the intent to force them to undergo ETT. Surface wind fields and other environmental characteristics (minimum pressure, latitude, thermal wind components, geopotential thickness and others) were analysed to determine the phase of ETT. This case study is the first example of Hybrid WRF being used to examine ETT, and while the steering flow did move the tropical cyclones into the extratropics as intended, only one storm was observed to undergo ETT. Further development of the code for Hybrid WRF is underway, with improvements in the initial and boundary conditions identified as a means to improve the representativeness of these experiments. Based on these simulated events, we intend to develop time-evolving, storm-centred wind fields, as well as statistics on cyclone phase space parameters that can be used to determine the stage of transition to be used in a future stochastic-parametric model of tropical cyclones. Abstract submitted to/presented at the 22nd International Congress on Modelling and Simulation 2017 (MODSIM2017) - https://www.mssanz.org.au/modsim2017/

The Atmospheric Tomography software is a command line tool written in python to estimate the emission rate of a point source from concentration data. It implements an extension of the Bayesian inversion method. Bhatia, S., Feitz, A. and Francis, A. (2017) Atmospheric Tomography, GitHub repository, https://github.com/GeoscienceAustralia/atmospheric_tomography_laser

Here we demonstrate a workflow for the development of a local, corrected wind field for severe Tropical Cyclone (TC) Debbie. We combine modelling with corrections based on observations, and local wind effects including topography, land cover, shielding and direction to provide the best estimate of actual wind speeds. This is important, as wind speed observations are sparse, and do not necessarily provide even coverage of the TC landfall region. The final corrected wind field records the maximum 0.2 second wind gust, at 10 metres above ground, throughout the lifetime of TC Debbie, and provides a best estimate of maximum wind gust speeds associated with TC Debbie. Through the development of this workflow we will demonstrate the importance of observational data for validating wind field modelling outputs, and highlight the usefulness of James Cook University’s mobile anemometers for collecting wind speed data where gaps exist in the Bureau of Meteorology’s automatic weather station network. We identify the limitations in the availability of national land cover datasets at high resolution, and demonstrate the development of a fit-for-purpose land cover dataset using GA’s Digital Earth Australia Landsat archives (Lewis et al. 2017). This report and the accompanying datasets have been released with the aim of showcasing a method, which can be refined by others to develop a standard methodology for the production of local TC wind fields. This workflow can be applied in the same way following future TC events to support the post-disaster field surveys that are routinely carried out by a range of parties following a severe TC making landfall. The local wind fields, combined with the damage surveys ultimately help to refine our vulnerability models of housing stock in Australia.

Here we report on the application of a new CO2 quantification and localization technique, called atmospheric tomography. The results of the study indicate that, through careful data processing, measurements from the comparatively inexpensive but lower accuracy and lower precision CO2 sensor array can provide useful data. Results from the application of the tomography technique will be presented and limitations of the technique discussed. From the 9th International Carbon Dioxide Conference, Beijing, China

To provide the solar power industry with a data resource to allow them to assess the economic potential of a site for a solar power plant. Specifically under the Solar Flagship program.

The ultimate purpose of carbon capture and storage is to keep CO2 out of the atmosphere. However there are some scenarios in which leakage to atmosphere may occur. Because of the large and variable level of naturallyoccurring CO2 , and rapid dispersion in the atmosphere, leakage to atmosphere can be difficult to detect from concentration measurements. By using prior information from risk assessments about plausible location of leaks, it is possible to design simple yet effective systems for identifying the location of a leak within a pre-defined area of surveillance. We have designed an inexpensive system of autonomous sensors that can locate leaks of CO2 , and have tested it during a controlled release at the CO2CRC Otway site. The system proved effective and it, and its associated workflow, could be adapted and implemented in a variety of storage settings.

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

Geoscience Australia and the CO2CRC have constructed a greenhouse gas controlled release facility at an experimental agricultural station maintained by CSIRO Plant Industry at Ginninderra, Canberra. The facility is designed to simulate surface emissions of CO2 from the soil into the atmosphere and is modelled on the ZERT controlled release facility in Montana. Injection of CO2 into the soil is via a 120 m long slotted HDPE pipe installed horizontally 2 m underground. An eddy covariance (EC) system was installed at Ginninderra during the first sub-surface release (March - June 2012). The EC system, which generated 15 minute averages using a 10 Hz sampling frequency, measured net radiation (as a function of upwelling and downwelling, solar and longwave radiation); wind speed and direction in 3 dimensions; CO2 and H2O concentration; and temperature and pressure. The EC system was installed to provide baseline atmospheric measurements and assess methods for quantifying CO2 leakages. The daily CO2 release rate was 100 kg/d. Here we report on the application of the CO2 emissions quantification method developed by Pan et al. (2010) for detecting and quantifying CO2 leakages using EC techniques. The approach seeks to isolate the CO2 leakage signal from the natural variation inherent in flux data, using a time-window splitting scheme, median filtering and scaling techniques. Results from application of the EC method at the Ginninderra site will be presented and modifications to the method and its limitations discussed. Pan, L.; Lewicki, J.L.; Oldenburg C.M.; and Fischer M.L., (2010). Time-window based filtering method for near-surface detection of leakage from geological carbon sequestration sites, Environmental Earth Sciences, 60, pp 359-369. Proceedings of the 2013 International Carbon Dioxide Conference - Beijing China