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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. This paper presents and explores 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 optimisation problem with a tangent linear approach and  forward automatic differentiation. Its potential is investigated using synthetic data  and  evaluated in different wildfire scenarios. The results show the capacity of the method to  quickly predict the location of the fire front with a positive lead time (ahead of the  event) in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our  method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and  computationally cheap so it can be used in mobile systems for field deployment and operativeness.  Thus, we put emphasis on producing a positive lead time and the means to maximise it

Forecasting wind-driven wildfires using an inverse modelling approach