<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

Hourly direct normal solar exposure is the total amount of direct beam solar energy falling over one hour on a surface whose orientation is maintained perpendicular to the solar beam. Typical values for hourly direct normal exposure range up to around 3 MJ/m2 (megajoules per square metre). The values are usually highest in clear skies and decrease rapidly with increasing cloudiness, and also decrease to a lesser extent with increasing haziness and decreasing solar elevation. Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day. Typical values for daily global exposure range from 1 to 35 MJ/m2 (megajoules per square metre). For mid-latitudes, the values are usually highest in clear sun conditions during the summer and lowest during the winter or very cloudy days. See LINEAGE below for more information.

Wind multipliers are factors that transform regional wind speeds to local wind speeds considering local effects of land cover and topographic influences. It includes terrain, shielding, topographic and direction multipliers. Except for the direction multiplier whose value can be defined specifically by the Australian wind loading standard AS/NZS 1170.2, terrain, shielding and topographic multipliers are calculated using this software package based on the adaptations of formulae outlined in the AS/NZS 1170.2. This package is an upgraded version of wind multiplier computation software (<a href="https://pid.geoscience.gov.au/dataset/ga/82481">eCat 82481</a>) used to produce wind terrain, shielding and topographic multipliers for national coverage using an input of Land Cover Classification Scheme (LCCS) level 4 version 1.0.0 ( 2015) and 1-second SRTM level 2 derived digital elevation models (DEM-S) version 1.0. In order to improve the classification resolution in the built environment, the LCCS layer is overlaid with both mesh block and settlement types. The output is based on tiles with dimensions about 1 by 1 decimal degree in netCDF format. It includes terrain, shielding and topographic multiplier respectively. Each multiplier further contains 8 directions. The upgraded package is stored in Geoscience Australia public-facing repository and can be accessed via https://github.com/GeoscienceAustralia/Wind_Multipliers

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<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>

The Tropical Cyclone Scenario Selector Tool (TC SST) provides an interactive application to interrogate the stochastic event catalogue which underpins the 2018 Tropical Cyclone Hazard Assessment (TCHA18). The application allows users to search for TC events in the catalogue based on location and intensity (either TC intensity category, or maximum wind speed), visualise the tracks and the wind fields of those events, and download the data for further analysis.

The Australian Solar Energy Information System V3.0 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 V3.0 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 V3.0 has additional solar exposure data provided by the Bureau of Meteorology. - Australian Daily Gridded Solar Exposure Data now ranges from 1990 to 2013 - Australian Monthly Solar Exposure Gridded Data now ranges from 1990 to 2013 - Australian Hourly Solar Exposure Gridded Data now ranges from 1990 to 2012 ASEIS V3.0 also has a new electricity transmission reference dataset which allows for information to be assessed on any chosen region against the distance to the closest transmission powerline.

Global solar exposure is the total amount of solar energy falling on a horizontal surface. The hourly global solar exposure is the total solar energy for one hour. Typical values for hourly global exposure range up to 4 MJ/m2 (megajoules per square metre). The values are usually highest in the middle of the day and around summer, with localised variations caused mainly by variations in atmospheric conditions, primarily cloudiness. See metadata statement for more information.

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.

Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day. Typical values for daily global exposure range from 1 to 35 MJ/m2 (megajoules per square metre). For mid-latitudes, the values are usually highest in clear sun conditions during the summer, and lowest during winter or very cloudy days. The monthly means are derived from the daily global solar exposure. See metadata statement for more information.