1. A Global Probability‐Of‐Fire (PoF) Forecast.
- Author
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McNorton, J. R., Di Giuseppe, F., Pinnington, E., Chantry, M., and Barnard, C.
- Subjects
WILDFIRES ,WILDFIRE prevention ,NUMERICAL weather forecasting ,FIRE risk assessment ,WEATHER ,WEATHER forecasting ,FORECASTING - 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. Plain Language Summary: Wildfires have widespread effects on local ecosystems, communities, air quality, and global atmospheric conditions. Accurate wildfire forecasts can be used by local communities and agencies to manage and respond to wildfires effectively. As such, it is essential these predictions are not only accurate but are accessible in real‐time and provide sufficient advanced notice to ensure successful actions can be taken. To achieve this we have developed a forecasting system that combines satellite observations, weather forecasting, and vegetation characteristics using machine learning. Tested on historical data and operated in real‐time, our model provides a global daily wildfire forecast with a 9 km resolution, predicting the likelihood of fires up to 30 days in advance. The model outperforms existing fire danger forecasts when evaluated against satellite observations of active fires. It can also identify key drivers which result in the occurrence of fire. Finally, it not only offers real‐time forecasts but can be used to help investigate past fire events, understand their causes, and predict wildfire activity over longer climate timescales. Key Points: A data‐driven model informed by satellite observations and an Earth System Model provides accurate fire forecasts upto 10 days in advanceThe probability of fire forecast is implemented operationally in a numerical weather prediction model to provide real‐time forecastsFire attribution is demonstrated and can be used for specific fire events or historical analysis [ABSTRACT FROM AUTHOR]
- Published
- 2024
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