A Global Probability‐Of‐Fire (PoF) Forecast

Autor: J. R. McNorton, F. Di Giuseppe, E. Pinnington, M. Chantry, C. Barnard
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geophysical Research Letters, Vol 51, Iss 12, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2023GL107929
Popis: 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 ∼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.
Databáze: Directory of Open Access Journals
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