Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index

In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biom...

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Bibliographic Details
Published in:Journal of applied meteorology and climatology 2017-10, Vol.56 (10), p.2789-2799
Main Authors: Di Giuseppe, Francesca, Rémy, Samuel, Pappenberger, Florian, Wetterhall, Fredrik
Format: Article
Language:English
Subjects:
Aerosol concentrations
Aerosols
Air pollution
Atmospheric chemistry
Atmospheric composition
Atmospheric models
Atmospheric monitoring
Biomass
Biomass burning
Burning
Carbon
Climate models
Daily forecasts
Dynamics
Emissions
Fire danger
Fire hazards
Fire weather
Fires
Forest & brush fires
Hazards
Humidity
Moisture content
Rain
Statistical analysis
Statistical methods
Time dependence
Weather
Weather forecasting
Weather index
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Summary:In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time dependent factor ℳ, which modulates the amount of aerosol emitted from fires during the forecast. The factor ℳ is related to the daily change in fire danger conditions and is a function of the fire weather index (FWI). The impact of the new scheme was tested in the atmospheric composition model managed by the CAMS. Experiments from 5 months of daily forecasts in 2015 allowed for both the derivation of global statistics and the analysis of two big fire events in Indonesia and Alaska, with extremely different burning characteristics. The results indicate that time-modulated emissions based on the FWI calculations lead to predictions that are in better agreement with observations.
ISSN:1558-8424
1558-8432
DOI:10.1175/JAMC-D-16-0405.1