Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index

Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index AbstractIn 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 , which modulates the amount of aerosol emitted from fires during the forecast. The factor 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
D.O.I.
10.1175/JAMC-D-16-0405.1
Publisher site
See Article on Publisher Site

Abstract

AbstractIn 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 , which modulates the amount of aerosol emitted from fires during the forecast. The factor 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.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Oct 15, 2017

References

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