Fugitive methane emissions, in particular relating to coal seam gas (CSG),has become an emerging issue in Australia over the last few years. There has been significant controversy in US regarding the magnitude of fugitive emissions during production from unconventional gas wells, with large differences in emissions reported between studies using different measurement approaches. . Preliminary research into a small number of Australia's unconventional fields suggest the average fugitive emissions per well are lower than that found in the US. The primary challenge is that the techniques for quantifying methane leakages are still at an early stage of development. Current methods for the small to medium scale use chamber based approaches or vehicles installed with fixed sampling lines and high precisions gas analysers. These technologies are promising, but generally have not been ground truthed in field conditions against known emission rates to estimate effectiveness. They also have limited application in environments where vehicle access is not possible. The Ginniderra facility is being upgraded to support a methane controlled release experiment in 2015. This will enable testing of and verifying methods and technologies for measuring and quantifying methane emissions. To address the absence of suitable techniques for emmission measurement at medium scales, several BOREAL lasers will be deployed which work at scales of 20-1000 m. It is also envisaged airborne techniques utilising laser and hyperspectral will be deployed, along with tomography work utilising multiple concurrent concentration measurements.

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.

Data provided by AIR Worldwide to Geoscience Australia as part of the review of the PCRAFI Phase II project, which examined hazard and risk from TCs and earthquakes in the Pacific. The review was conducted in 2010. This data should be considered in-confidence and is not for distribution or external use.

The Australian Solar Energy Information System V2.0 has been developed as a collaborative project between Geoscience Australia and the Bureau of Meteorology. The product provides pre-competitive spatial information for investigations into suitable locations for solar energy infrastructure. The outcome of this project will be the production of new and improved solar resource data, to be used by solar researchers and the Australian solar power industry. it is aimed to facilitate broad analysis of both physical and socio-economic data parameters which will assist the solar industry to identify regions best suited for development of solar energy generation. It also has increased the quality and availability of national coverage solar exposure data, through the improved calibration and validation of satellite based solar exposure gridded data. The project is funded by the Australian Renewable Energy Agency. The ASEIS V2.0 has a solar database of resource mapping data which records and/or map the following Solar Exposure over a large temporal range, energy networks, infrastructure, water sources and other relevant data. ASEIS V2.0 has additional solar exposure data provided by the Bureau of Meteorology. - Australian Daily Gridded Solar Exposure Data now ranges from 1990 to 2012 - Australian Monthly Solar Exposure Gridded Data now ranges from 1990 to 2011 ASEIS V2.0 also has a new electricity transmission reference dataset which allows for information to be assessed on any chosen region the distance and bearing angle to the closest transmission powerline.

A metadata report for the atmospheric monitoring station installed in Arcturus, south of Emerald in central Queensland. The station was installed for baseline atmospheric monitoring to contribute to emission modelling spanning 2010-2014. The station included compositional gas analysers, supporting meteorological sensors and an eddy covariance flux tower. The metadata covered in the report include: the major variables measured by each instrument, the data duration and frequency, data accuracy, calibration and corrections, the location the data is stored, and the primary contact for the data.

<div>An automatic algorithm for classifying wind gust events has been developed at Geoscience Australia, utilizing 1-minute weather observations from Automatic Weather Stations (AWS). This algorithm employs a comprehensive dataset of wind, temperature, dew point, and pressure measurements within a two-hour timeframe centred on the peak wind gust.&nbsp;&nbsp;</div><div> The classification methodology effectively segregates wind gust events into convective and non-convective categories. Initial development entails a subset of stations, employing visual classification verified by contemporaneous observer reports and weather radar data, to create a robust training dataset. The algorithm, based on the analysis of almost 1000 visually-classified events, demonstrates the capability to classify over 150,000 events in a matter of minutes.&nbsp;</div><div> Utilizing wind gust events from past 20 years via our algorithm, the spatial distribution, diurnal cycle and seasonal variation are investigated across Australia. Moreover, a comparative analysis of spatial and temporal disparities, along with radar characteristics, has been conducted for convective and non-convective gust events. Finally, the extreme values of wind gust events, including the 1% annual exceedance probability wind speed (using the Generalized Pareto Distribution) across Australia is shown in this presentation. &nbsp;</div> Presented at the 30th Conference of the Australian Meteorological and Oceanographic Society (AMOS) 2024

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.

Using the wind multiplier code (https://pid.geoscience.gov.au/dataset/ga/82481) and an appropriate source of classified terrain data, wind multipliers for all of Queensland at (approximately) 25 metre resolution were created. The wind multipliers have been used to guide impact assessments as part of the Severe Wind Hazard Assessment for Queensland.

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

No abstract available