<div>This database presents classified wind gust events for all Australian Automatic Weather Stations, based on semi-automatic classification of 1-minute observations of wind gust speed, temperature, dew point and station pressure. Wind events are classified based on the temporal evolution of the weather variables, using convolutional kernel transforms. Additional attributes include a number of derived variables (e.g. rainfall preceding and following the gust event), contemporaneous weather phenomena and binary classifications from a range of authors. </div><div><br></div><div>The main classification is described by Arthur, Hu and Allen (submitted to <em>Natural Hazards</em>, 2024). </div><div><br></div><div>Weather observation data are provided by the Bureau of Meteorology. Lightning data (2004-2024) was provided by TOA Systems Global Lightning Network. </div>

<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

<div>The wind hazard climate in South East Queensland is a combination of tropical cyclones, thunderstorms and synoptic storms. This dataset provides estimated average recurrence interval (ARI) or annual exceedance probability (AEP) wind speeds over the region, based on an evaluation of observational (thunderstorms and synoptic winds) and simulated data (tropical cyclones). </div><div><br></div><div>The tropical cyclone wind hazard was evaluated using Geoscience Australia's Tropical Cyclone Risk Model (TCRM), which provides a spatial representation of the AEP wind speeds arising from tropical cyclones. Thunderstorm wind hazard was evaluated from analysis of observed wind gusts across South East Queensland, aggregated into a single 'superstation' to provide a single representative hazard profile for the region.</div><div><br></div><div>The resulting combined wind hazard estimates reflect the dominant source of wind hazard in South East Queensland for the most frequent events (exceedance probabilities greater than 1:50) is thunderstorm-generated wind gusts. For rarer events, with exceedance probabilities less than 1:200, TC are the dominant source of extreme gusts.&nbsp;</div><div><br></div><div>Local effects of topography, land cover and the built environment were incorporated via site exposure multipliers (Arthur & Moghaddam, 2021), which are based on the site exposure multipliers defined in AS/NZS 1170.2 (2021).</div><div><br></div><div>The local wind hazard maps were used to evaluate the financial risk to residential separate houses in South East Queensland.</div><div><br></div><div>Wind speeds are provided for average recurrence intervals ranging from 1 year to 10,000 years. No confidence intervals are provided in the data. </div>

<div>The Severe Wind Hazard Assessment for South East Queensland (SWHA-SEQ) analysed risk from severe wind events in a marginal tropical cyclone (TC) region with a large exposed population, and historical severe thunderstorm and TC impacts. SWHA-SEQ was a collaborative effort bringing together 15 partners across government, academia and the insurance sector to improve the collective understanding of wind risk in the region and inform future strategies to reduce this risk, in the context of climate change, urban planning and socio-economic status of the population. </div><div>The project involved enhancing the understanding of hazard, exposure and physical vulnerability to strengthen the comprehension of risk, including local-scale wind hazard from thunderstorm and TC wind gusts, and a semi-quantitative analysis of future wind hazard. Structural characteristics of residential housing stock were updated through a combination of street surveys, national databases of built assets and insurance portfolio statistics. Vulnerability models for residential houses including retrofitted models for 5 common house types were developed, alongside identification of key vulnerability factors for residential strata buildings.</div><div>Local governments are building on the outcomes of the project, with the City of Gold Coast using the project outcomes as the key evidence base for a A$100m investment over 7 years to advocate for uplift of building design criteria, targeted community engagement and resilience of City-owned infrastructure. Other local governments have conducted specific exercises exploring how they would manage a severe TC impact. The investments and activities directly flowing from SWHA-SEQ are testament to the partner engagement through the project. Presented at the 2024 Symposium on Hurricane Risk in a Changing Climate (SHRCC2024)

<div>South East Queensland (SEQ) is exposed to a range of severe storms that generate damaging winds, including east coast lows, thunderstorms and tropical cyclones. The risk posed by these storms is not well understood and, in a region that hosts a large proportion of Queensland’s population and economic activity, it is important to understand these risks and the potential benefits of mitigation actions, particularly in the context of climate change, urban planning and the socio-economic status of the population. &nbsp;</div><div>The primary objectives of the Severe Wind Hazard Assessment for South East Queensland (SWHA-SEQ) project (October 2020 – December 2022) were to improve the understanding of current wind risk in SEQ and to develop actionable information to inform future strategies to reduce this risk. Collaboration across fifteen partners in local government, insurance, emergency management, State government and academia has delivered valuable and actionable insights into the risk and resilience of SEQ at a local scale. </div><div> We discuss the relative contributions of different wind storms to the hazard profile, local influences on hazard and risk, and the intersection with community resilience indicators that assist in formulating targeted mitigation strategies. SEQ has a range of landscapes that influence the local hazard, including heavily urbanized lands, semi-rural communities in complex terrain and beachfront or canal estates. These landscapes, and the attributes of the buildings in them, contribute to the risk profile in varied and complex ways. We also explore the intersection of high-risk areas with socio-economic status to identify priority areas for potential retrofit programs. Presented at the 30th Conference of the Australian Meteorological and Oceanographic Society (AMOS) 2024

Modelling the effectiveness of retrofit to legacy houses requires a quantitative estimate of the houses’ vulnerability to severe wind and how the vulnerability is affected by mitigation work. Historical approaches to estimating vulnerability through either heuristic or empirical methods do not quantitatively capture the change in vulnerability afforded by mitigation. To address this information gap the Bushfire and Natural Hazards CRC project “Improving the Resilience of Existing Housing to Severe Wind Events” has augmented a software tool which models damage from wind loads and associated repair cost. In this paper the development process is described including the establishment of a suite of test cases to assess the effectiveness of the software. An example of the validation work is presented along with the augmentation of the software from the previous version. Finally, use of the software in assessing the incremental effectiveness of a range of mitigation strategies in economic terms is described. Abstract submitted to/presented at the19th Australasian Wind Engineering Society Workshop.

This National Wind Risk Assessment (NWRA) has been undertaken as a component of the National Climate Risk Assessment, which is led by the Department of Climate Change, Environment, Energy and Water (DCCEEW) and drawing upon the expertise in the Australian Climate Service. The NWRA sits alongside similar risk assessments for flood and peri-urban bushfire hazards. This report provides a summary of the workflow, the contributing elements and a cursory analysis of the resulting risk metrics and is intended for downstream users of the data to understand it’s derivation. A more detailed examination of the results is to be completed at a later date. The NWRA is a quantitative evaluation of the likelihood and magnitude of the physical impacts to the built domain arising from severe wind events. The focus of the assessment is residential separate houses, due to the limited scope of required data to calculate the impacts of these events. It is anticipated that the NWRA will represent one of the most advanced assessments performed in the NCRA, as the available information enables us to work through the hazards, exposed assets, and their vulnerabilities to quantify the impacts from severe wind events. This dataset provides estimated Average Annual Loss values for all ASGS (2021) SA1 regions, based on the wind impacts to residential separate houses. The analysis workflow is fully described in the accompanying document. <b>Note:</b> This product was change to internally available 14 November 2024 as directed by the Australian Climate Services. This status will be reviewed March 2025.

<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