Meteorological data from Arcturus (ARA) atmospheric greenhouse gas baseline station. Data includes time stamp (local time), air temperature, relative humidity, wind speed, wind direction, sigma-theta, solar radiation, barometric pressure and total rainfall. Dataset limited to 15 min and 60 min average data from12/6/13 to 21/6/13.

This report contains the preliminary results of Geoscience Australia survey 273 to northwest Torres Strait. This survey was undertaken as part of a research program within the Torres Strait CRC aimed at understanding marine biophysical processes in Torres Strait and their effect on seagrass habitats. Two Geoscience Australia surveys were undertaken as part of this program, survey 266 measured monsoon season conditions (Heap et al., 2005), and survey 273 measured trade wind conditions. Section 6 compares and contrasts the survey results acquired for both surveys. Section 7 addresses the results of the survey program in light of the objectives of the CRC proposal. Survey 273 acquired numerous different data types to assist with characterising the mobile sediments and hydrodynamic nature of the region. Multibeam sonar, current meters, grab samples, vibro-cores, underwater video, meteorological data (from the Bureau of Meteorology), Landsat imagery, were all used to characterise the seabed hydrodynamics of Torres Strait.

Geoscience Australia's Risk & Impact Analysis Group has developed a statistical model of wind hazard utilising the Generalised Pareto Distribution (GPD). The model calculates the return period of severe winds based on daily maximum wind gust observations. The model utilises an automated procedure to partition the data into the hazard constituents (thunderstorms, synoptic winds, tornadoes, etc) based on the World Meteorological Observation Codes 3-hourly coded observations. This observational data set records the archived and present weather at the station site. The model fits the GPD to the station data (daily maximum wind gust) by automating the selection of the appropriate threshold above which data is included in the extreme value distribution. This threshold <em>u</em> is selected as the maximum of all feasible return period values obtained by fitting the GPD. Published comparative findings, including same region results, demonstrate the model can produce similar results in a more efficient, fully computational way. Confidence intervals for return periods are calculated automatically to allow wind analysts to distinguish regions of greater reliability.

A short animation of an atmospheric simulation of methane emissions from a coal mine (produced using TAPM) compared to actual methane concentrations detected by the Atmospheric Monitoring Station, Arcturus in Central Queensland. It illustrates the effectiveness of both the detection and simulation techniques in the monitoring of atmospheric methane emissions. The animation shows a moving trace of both the simulated and actual recorded emissions data, along with windspeed and direction indicators. Some data provided by CSIRO Marine and Atmospheric Research.

A model to assess severe wind hazard using climate-simulated wind speeds have been developed at Geoscience Australia (Sanabria and Cechet, 2010a). The model has a num-ber of advantages over wind hazard calculated from observational data: Firstly the use of climate-simulated data makes it possible to assess wind hazard over a region rather than at a recording station. Secondly climate-simulated data allows wind analysts to calculate wind hazard over a long climatology and, more importantly, to consider the impact of cli-mate change on wind hazard. In this paper we discuss model sensitivity to two IPCC scenarios: scenario B1, a low emissions scenario, and scenario A2, a high emissions scenario. Current and future climate is considered. Currently we deal only with gusts associated with synoptic winds (mid-latitude weather systems) as the climate model only provides mean winds at a resolution of 14 km, which does not resolve thunderstorms. MODEL DESCRIPTION The model involves three computationally processes: - Calculation of return period (RP) for gust wind speed using a statistical model; - Extraction of wind speeds from a high resolution climate model; and - A Monte Carlo method to generate synthetic gust speeds based on a convolution of modelled mean speeds and empirical gust factor measurements.

Meteorological data from the Arcturus (ARA) atmospheric greenhouse gas baseline station. Data includes time stamp (local time), air temperature, relative humidity, wind speed, wind direction, sigma, solar radiation, barometric pressure and rainfall total. Dataset limited to the 1/6/12 to 8/7/12.

A review commissioned by the Council of Australian Governments (COAG) in June 2001 entitled 'Natural Disasters in Australia: reforming mitigation, relief and recovery arrangements' concluded that a new approach to natural disasters in Australia was needed. While disaster response and reaction plans remain important, there is now a greater focus towards anticipation of mitigation against natural hazards, involving a fundamental shift in focus beyond relief and recovery towards cost-effective, evidence-based disaster mitigation. This new approach now includes an assessment of the changes in frequency and intensity of natural hazard events that are influenced by climate change, and aims to achieve safer, more sustainable Australian communities in addition to a reduction in risk, damage and losses from future natural disasters. Geoscience Australia (GA) is developing risk models and innovative approaches to assess the potential losses to Australian communities from a range of sudden impact natural hazards. GA aims to define the economic and social threat posed by a range of rapid onset hazards through a combined study of natural hazard research methods and risk assessment models. These hazards include earthquakes, cyclones, floods, landslides, severe winds and storm surge/tsunami. This presentation provides an overview of the risk that peak wind gusts pose to a number of Australian communities (major capital cities), and for some cities examines how climate change may affect the risk (utilising modelling underpinned by a small subset of the IPCC greenhouse gas emission scenarios).

This user guide describes the important instructions for using the Tasmanian Extreme Wind Hazard Standalone Tool (TEWHST). It aims to assist the Tasmanian State Emergency Service (SES) to view the spatial nature of extreme wind hazard (and how it varies depending on the direction of the extreme wind gusts). This information indicates detailed spatial texture for extreme hazard, which can provide guidance for understanding where the local-scale hazard (and impact) is expected to be the greatest for any particular event depending on the intensity and directional influence of the broad-scale severe storm. The tool provides spatial information at the local scale (25 metre resolution) of the return period extreme wind hazard (3-second gust at 10 metre height; variation with direction) where the broad-scale regional hazard is provided by the Australian and New Zealand Wind Loading Standard (AS/NZS 1170.2, 2002).

A statistical downscaling approach is used to compare changes in environmental indicators of tropical cyclone characteristics between three greenhouse gas emissions scenarios in the Australian region, using results from models used for the IPCC 4th Assessment Report. Maximum potential intensity is shown to change linearly with global mean temperature, independent of emissions scenario, with a 2-3% increase per degree of global warming in Australia's tropical regions. Changes in vertical wind shear are more ambiguous, however the magnitude of changes in tropical cyclone genesis regions is small. The genesis potential index increases significantly in all scenarios, and appears to be driven by the increase in MPI. Results for Australia's tropical regions suggest that tropical cyclone intensity is highly likely to increase with global warming, while results for frequency are suggestive of a frequency increase, but less conclusive. Further work to assess frequency changes will allow quantification of changes in tropical cyclone hazard under climate change.

Knowledge of the degree of damage to residential structures expected from severe wind is used to study the benefits from adaptation strategies developed in response to expected changes in wind severity due to climate change, inform the insurance industry and provide emergency services with estimates of expected damage. A series of heuristic wind vulnerability curves for Australian residential structures has been developed for the National Wind Exposure project. In order to provide rigor to the heuristic curves and to enable quantitative assessment to be made of adaptation strategies, work has commenced by Geoscience Australia in collaboration with James Cook University and JDH Consulting to produce a simulation tool to quantitatively assess damage to buildings from severe wind. The simulation tool accounts for variability in wind profile, shielding, structural strength, pressure coefficients, building orientation, component self weights, debris damage and water ingress via a Monte Carlo approach. The software takes a component-based approach to modelling building vulnerability. It is based on the premise that overall building damage is strongly related to the failure of key components (i.e. connections). If these failures can be ascertained, and associated damage from debris and water penetration reliably estimated, scenarios of complete building damage can be assessed. This approach has been developed with varying degrees of rigor by researchers around the world and is best practice for the insurance industry. This project involves the integration of existing Australian work and the development of additional key components required to complete the process.