Articles | Volume 22, issue 4
Nat. Hazards Earth Syst. Sci., 22, 1335–1346, 2022
https://doi.org/10.5194/nhess-22-1335-2022

Special issue: The role of fire in the Earth system: understanding interactions...

Nat. Hazards Earth Syst. Sci., 22, 1335–1346, 2022
https://doi.org/10.5194/nhess-22-1335-2022
Research article
13 Apr 2022
Research article | 13 Apr 2022

Forecasting the regional fire radiative power for regularly ignited vegetation fires

Tero M. Partanen and Mikhail Sofiev

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Cited articles

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Coughlan, R., Di Giuseppe, F., Vitolo, C., Barnard, C., Lopez, P., and Drusch, M.: Using machine learning to predict fire-ignition occurrences from lightning forecasts, Meteorol. Appl., 28, e1973, https://doi.org/10.1002/met.1973, 2021. a
Di Giuseppe, F., Rémy, S., Pappenberger, F., and Wetterhall, F.: Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index, J. Appl. Meteorol. Clim., 56, 2789–2799, https://doi.org/10.1175/JAMC-D-16-0405.1, 2017. a
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The presented method aims to forecast regional wildfire-emitted radiative power in a time-dependent manner several days in advance. The temporal fire radiative power can be converted to an emission production rate, which can be implemented in air quality forecasting simulations. It is shown that in areas with a high incidence of wildfires, the fire radiative power is quite predictable, but otherwise it is not.
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