As part of the 2018 Tropical Cyclone Hazard Assessment (TCHA), we compiled the geospatial raster dataset that can be accessible to internal and external users via ArcGIS online and can be integrated for building additional geoprocessing applications. This web service gives more stable and easy access to data and interactive maps. With having separate geospatial layers for each recurrence interval- i.e. 5 through 10000 years, users can toggle between the layers and evaluate the changes in wind speed (km/hr) and potential areas at risk on the fly.

<p>A methane (CH4) and carbon dioxide (CO2) release experiment was held from April – June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. This dataset contains quality controlled 5 minute averaged CH4 concentration and meteorlogical data from 21 May to 12 June for 4 Eddy Covariance towers, 1 scanning Boreal laser, 2 scanning FTIR instruments and 2 Picarro towers. <p>This dataset accompanies the article: Cartwright, L., Zammit-Mangion, A., Bhatia, S., Schroder, I., Phillips, F., Coates, T., Neghandhi, K., Naylor, T., Kennedy, M., Zegelin, S., Wokker, N., Deutscher, N. and Feitz, A. (2019) Bayesian atmospheric tomography for detection and estimation of methane sources: Application to data from the Ginninderra 2015 release experiment, Atmospheric Measurement Techniques (submitted) <p>Dataset citation: <p>Feitz, A., Schroder, I., Phillips, F., Coates, T., Neghandhi, K., Bhatia, S., Naylor, T., Kennedy, M,. Zegelin, S., Wokker, N., Deutscher, N.M., Cartwright, L. and Zammit-Mangion, A. (2019) The 2015 Ginninderra CH4 and CO2 release experiment: Fixed and scanning sensor dataset, Geoscience Australia, DOI: http://dx.doi.org/10.26186/5cb7f14abd710

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established. <b>Citation:</b> Li, F.; Jupp, D.L.B.; Schroeder, T.; Sagar, S.; Sixsmith, J.; Dorji, P. Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-BasedWater-Leaving Reflectance Using Match-Up Sites in Australian CoastalWaters. Remote Sens. 2021, 13, 1927. https://doi.org/10.3390/rs13101927

<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.