A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. In this recent paper that we have published [*] in the journal Natural Hazards and Earth System Sciences, we present and explore a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling.
The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimization problem with a tangent linear approach and forward automatic differentiation.
[*] O.Rios, W. Jahn, G. Rein, Forecasting wind-driven wildfires using an inverse modelling approach, Natural Hazards and Earth System Sciences 14, pp. 1491-1503, 2014.
http://dx.doi.org/10.5194/nhess-14-1491-2014 (open access)