Meteorological data from the Arcturus (ARA) atmospheric greenhouse gas baseline station. Data includes time stamp (local time), air temperature, relative humidity, wind speed, wind direction, sigma, solar radiation, barometric pressure and rainfall total. Dataset limited to the 1/6/12 to 8/7/12.

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

Meteorological data from Arcturus (ARA) atmospheric greenhouse gas baseline station. Data includes time stamp (local time), air temperature, relative humidity, wind speed, wind direction, sigma-theta, solar radiation, barometric pressure and total rainfall. Dataset limited to 15 min and 60 min average data from12/6/13 to 21/6/13.

Australian present and past weather data as produced by the Bureau of Meteorology. Dataset contains: Present weather data as international code; Past weather data as international code; plus additional supporting information.

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.

National meteorological and hydrological services (NMHSs) provide severe weather warning information to inform decision-making by emergency management (EM) services and to allow communities to take defensive and mitigation action prior to and during severe weather events. Globally, warning information issued by NMHSs varies widely from solely hazard-based to impact-based forecasting encompassing the exposure and vulnerability of communities to severe weather. The most advanced of these systems explicitly and quantitatively model the impacts of hazards on sectors of interest. Incorporating impact information into severe weather warnings contextualises and personalises the warning information, increasing the likelihood that individuals and communities will take preparatory action. This paper reviews a selection of current efforts towards severe weather warnings and impact forecasting capabilities globally and highlights uncertainties that currently limit forecasts and modelling of multi-hazard events.