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\begin{document}

\title{A Global Probability-of-Fire (PoF) Forecast}



\author[1]{Joe Ramu McNorton}%
\author[2]{Francesca Di Giuseppe}%
\author[3]{Ewan Mark Pinnington}%
\author[2]{Matthew Chantry}%
\author[2]{Chris Barnard}%
\affil[1]{European Centre for Medium-Range Weather Forecasts}%
\affil[2]{ECMWF}%
\affil[3]{National Center for Earth Observation, Department of Meteorology, University of Reading}%


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  \date{June 10, 2024}


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\begin{abstract}
Accurate wildfire forecasting can inform regional management and
mitigation strategies in advance of fire occurrence. Existing systems
typically use fire danger indices to predict landscape flammability,
based on meteorological forecasts alone, often using little or no direct
information on land surface or vegetation state. Here, we use a
vegetation characteristic model, weather forecasts and a data-driven
machine learning approach to construct a global daily \textasciitilde{}9
km resolution Probability of Fire (PoF) model operating at multiple lead
times. The PoF model outperforms existing indices, providing accurate
forecasts of fire activity up to 10 days in advance, and in some cases
up to 30 days. The model can also be used to investigate historical
shifts in regional fire patterns. Furthermore, the underlying data
driven approach allows PoF to be used for fire attribution, isolating
key variables for specific fire events or for looking at the
relationships between variables and fire occurrence.%
\end{abstract}%



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\textbf{Hosted file}

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