Forest fires cause every year damages to biodiversity, atmosphere, and economy activities.
Forest fire simulation have improved significantly, but input data describing fire scenarios …

The extent and severity of bushfires in a landscape are largely governed by meteorological
conditions. An accurate understanding of the interactions of meteorological variables and …

Computational models of wildfires are necessary for operational prediction and risk
assessment. These models require accurate spatial fuel data and remote sensing …

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal
prediction of fire activity over these fire-prone regions remains a challenge and relies heavily …

With the advancement in scientific understanding and computing technologies, fire
practitioners have started relying on operational fire simulation tools to make better-informed …

Organisations that manage wildfires are expected to deliver scientifically defensible
decisions. However, the limited availability of high quality data restricts the rate at which …

Simulation is used to predict the spread of a wildland fire across land in real-time.
Nevertheless, the large uncertainties in these simulations must be quantified in order to …

Background Modelling of fire front progression is challenging due to the large range of
spatial and temporal scales involved in the interactions between the atmosphere and fire …

Computational simulations of wildfire spread typically employ empirical rate-of-spread
calculations under various conditions (such as terrain, fuel type, weather). Small …

Calculations of risk from natural disasters may require ensembles of hundreds of thousands
of simulations to accurately quantify the complex relationships between the outcome of a …