<div>The Severe Wind Hazard Assessment for Queensland arose as a project to better understand the potential impacts of tropical cyclones (TCs) on population centres and elements of critical infrastructure in Queensland. The rationale for the project was reinforced by lessons from Severe Tropical Cyclone (STC) Debbie, the direct and indirect impacts of which affected a significant area of Queensland, stretching from Bowen to the City of Gold Coast and Northern New South Wales between 28 March and 7 April 2017, resulting in 14 mostly flood associated deaths, and more than A$3.5 billion in direct losses. The intent of the project is to explore and assess a range of scenarios that extend beyond the contemporary recollection of severe events in order to challenge decision making for rarer but higher-consequence events. The scenarios described in the report can be used to improve planning for severe tropical cyclone (TC) events and their impacts. This includes developing a better understanding of how the capabilities of emergency services and supporting elements may be impacted in actual events.&nbsp;</div><div><br></div><div>Scenarios were selected from the catalogue of synthetic events (i.e. events that did not actually occur but whose occurrence was as probable as those that did occur) generated for the 2018 Tropical Cyclone Hazard Assessment (TCHA; Arthur, 2018), in consultation with Queensland Fire and Emergency Services (QFES) and those local governments involved within the project. Two TC events were modelled for each location for this project – a Category 3 and a Category 5 TC -with ‘favourable’ tracks for impact analysis. In all scenarios, consideration was given to regional historical analogues for the selected synthetic tracks to better relate the scenario outputs to known or “lived” events. These categories were chosen as they represent events with a moderate and very low likelihood with respect to intensity, based on historical observations. This also accounts for the future climate of less TCs but more intense occurrences, highlighting the different impacts arising from different events. It is important to emphasise and understand that each individual TC event will be different and lead to different impacts.&nbsp;</div><div><br></div>

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.

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.

<p>The wind hazard posed to Australia based on the frequency and intensity of tropical cyclones making landfall around the Australian coastline has been assessed using Tropical Cyclone Hazard Assessment (TCHA). This dataset is a derived product from the original raster layers. <p>We compiled geospatial raster layers for each recurrence interval - i.e. 5 through 10000 years in km/hr unit and classified in 19 classes to better present to a public audience.

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

Included fields: Bureau of Meteorology Station Number. Year month day in YYYY,MM,DD format. Present weather at (00, 03, 06, 09, 12, 15, 18, 21) hours Local Time, as international code. Quality of present weather at (00, 03, 06, 09, 12, 15, 18, 21) hours Local Time. Past weather at (00, 03, 06, 09, 12, 15, 18, 21) hours Local Time, as international code. Quality of past weather at (00, 03, 06, 09, 12, 15, 18, 21) hours Local Time.

Consider you are responsible for providing an emergency response in Karratha. There is a category 4 cyclone sweeping towards the coast and Bureau of Meteorology forecasts indicate the cyclone will intensify to category 5 before landfall. The last time a category 5 cyclone came close to Karratha was in 1999, when Cyclone John passed 80 km east of Karratha, sparing it the worst winds. If it had not turned to the southeast prior to landfall, damage to Karratha would have been much worse. Karratha has also grown substantially since then, with close to half the residential buildings constructed after 1997. As a first responder, are you prepared for the consequences of a direct strike? Do you even know what the extent of the impacts might be? What will Karratha look like immediately after the cyclone passes? If emergency preparation decisions were based on past experience, they would likely fall well short of the required action to minimise impacts. The Severe Wind Hazard Assessment project, funded through the WA Natural Disaster Resilience Program, endeavours to provide emergency managers with realistic, modelled scenarios of cyclone impact in WA communities to inform local, regional and state planning for cyclone risk. By analysing hypothetical scenarios, the Department of Fire and Emergency Services can identify and address gaps in the understanding of the impacts of a cyclone, and improve decision-making processes at coordination and control levels. A first step in this process is to develop hypothetical severe tropical cyclone footprints for WA communities. We use a stochastic tropical cyclone model to generate a catalogue of cyclone events, then select TC tracks meeting the criteria for the exercise: events with specific intensities passing directly over communities. Here we present the hazard footprints of these hypothetical storms, and a preliminary analysis of the impacts on residential buildings. Poster presented at the 2018 Amos-ICSHMO Conference Sydney, NSW (https://www.ametsoc.org/index.cfm/ams/meetings-events/ams-meetings/amos-icshmo-2018/)

<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

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.