Improving GFAS and CAMS biomass burning estimations by means of the Global ECMWF Fire Forecast system (GEFF)

The atmospheric composition analyses for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass burning fire emission estimates from the Global Fire Assimilation System (GFAS), which converts Fire Radiative Power (FRP) observations from MODIS satellites into smoke constituents. In case of missing observations GFAS relies on persistence, meaning that values of FRP observed the previous day are progressed in time until an observation is obtained. The statistical consequence of this assumption is an overestimation of fire duration which translates into an overestimation of fire emissions.

Also CAMS assumes persistence; meaning that biomass burning emissions calculated by the GFAS analysis are kept invariant during the forecast. This assumption is simple and practical in the absence of a dynamical fire model that could predict fire emissions evolution linked to the dynamical evolution of weather conditions and the available vegetation fuel. Nevertheless it can produce unrealistic aerosols and chemical predictions.

Since 2012 ECMWF has been involved in the development of the modeling components of the European Forest Fire Information System (EFFIS) which is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. Under the EFFIS umbrella, ECMWF has developed the Global ECMWF Fire Forecast (GEFF) system which models fire danger conditions and fire behaviors using the ECMWF Ensemble Prediction System (EPS).

Therefore, as an improvement to both the GFAS and CAMS systems we propose the synergistic use of the already existing GEFFs products to bring the benefit of a fire modeling component into the fire emission estimates. In the specific, in GFAS, the persistence assumption is replaced by the GEFF Canadian Forecast FireWeather Index (FWI) which diagnoses how atmospheric conditions affect the vegetation moisture content and ultimately the burning sustainability of fires. In CAMS, we propose the introduction of a modulation factor, M, to be applied to the emission sources during the forecast integration. Again, M is predicted from the daily variability of the FWI.

GFAS performance indicates that the FWI based model is a better tool than persistence to infer FRP at missing observation locations. Similarly, in CAMS, the use of the FWI based modulation factor has also a positive impact on the forecasted emissions.