To determine the magnitude of severe wind gust hazard due to thunderstorm downbursts using regional climate model output and analysis of observed data (including radar reflectivity and proximity soundings).

Wind speed Coefficients: 1) Fixed 2) Linear 3) Capped

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 Maps and Exposure report provide maps of three communities in Western Australia, with indicative BAL levels, and the aggregate inventory of assets and population exposed to the different levels of BAL.

In late 2012, Cyclone Evan swept across Samoa and Fiji, wreaking a path of destruction. Losses in Samoa were estimated at A$200 million - somewhere around 30% of Samoa's GDP. The capacity of small island states in the Pacific to recover from such large impacts is hampered by their small economies and comparatively high vulnerability to the impacts of natural hazards. What are the chances of an impact the size of Evan? And will the magnitude of those losses change under future climate scenarios due to changes in tropical cyclone activity? The Tropical Cyclone Risk Assessment in the Pacific Region project delivered information and methods for evaluating vulnerability and risks from tropical cyclones. This project was supported under the Pacific-Australia Climate Change Science and Adaptation Planning Program with co-financing from the Global Fund for Disaster Risk Reduction. A collaboration between the Australian Government Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education, Geoscience Australia and AIR Worldwide, the project drew together complementary skills to deliver an integrated and consistent risk assessment of likely damages to key infrastructure and assets in the Pacific from future tropical cyclones arising from severe winds and other hazards. This risk information will allow partner country governments to better integrate climate risk considerations into infrastructure development and ex-ante disaster planning. The presentation will detail the methods used in the analysis, and present outcomes of the risk assessment for current and future climate scenarios.

Tropical cyclones are the most common disaster in the Pacific, and among the most destructive. In December 2012, Cyclone Evan caused over US$200 million damage in Samoa, nearly 30 percent of Samoan GDP. Niue suffered losses of US$85 million following Cyclone Heta in 2004-over five times its GDP. As recently as January 2014, Cyclone Ian caused significant damage throughout Tonga, resulting in the first payout of the Pacific Catastrophe Risk Insurance Pilot system operated by the World Bank (2014). According to the Intergovernmental Panel on Climate Change (IPCC), intense tropical cyclone activity in the Pacific basin will likely increase in the future (IPCC 2013). But such general statements about global tropical cyclone activity provide little guidance on how impacts may change locally or even regionally, and thus do little to help communities and nations prepare appropriate adaptation measures. This study assesses climate change in terms of impact on the human population and its assets, expressed in terms of financial loss. An impact focus is relevant to adaptation because changes in hazard do not necessarily result in a proportional change in impact. This is because impacts are driven by exposure and vulnerability as well as by hazard. For example, a small shift in hazard in a densely populated area may have more significant consequences than a bigger change in an unpopulated area. Analogously, a dense population that has a low vulnerability to a particular hazard might not need to adapt significantly to a change in hazard. Even in regions with high tropical cyclone risk and correspondingly stringent building codes, such as the state of Florida, a modest 1 percent increase in wind speeds can result in a 5 percent to 10 percent increase in loss to residential property. Quantifying the change impact thus supports evidence-based decision making on adaptation to future climate risk.

Modelling tropical cyclone Yasi using TCRM

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

The Tropical Cyclone Impact Map provides guidance on areas likely to be impacted by severe winds due to tropical cyclones. The impact zones are generated by Geoscience Australia's Tropical Cyclone Risk Model (TCRM), and are based on the tropical cyclone forecast information published by the Bureau of Meteorology's Tropical Cyclone Warning Centres. TCRM applies a 2-dimensional parametric wind field to the forecast track provided by the Bureau of Meteorology, and translates the wind speeds into an indicator of potential damage to housing. Uncertainty in the forecast track is not included in the product.

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/)

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