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This paper investigates a potential of two remotely sensed wild-land firecharacteristics: 4-μm Brightness Temperature Anomaly (TA) and FireRadiative Power (FRP) for the needs of operational chemical transportmodelling and short-term forecasting of atmospheric composition and airquality. The treatments of the TA and FRP data are presented and amethodology for evaluating the emission fluxes of primary aerosols(PM2.5 and total PM) is described. The method does not include thecomplicated analysis of vegetation state, fuel load, burning efficiency andrelated factors, which are uncertain but inevitably involved in approachesbased on burnt-area scars or similar products. The core of the currentmethodology is based on the empirical emission factors that are used toconvert the observed temperature anomalies and fire radiative powers intoemission fluxes. These factors have been derived from the analysis ofseveral fire episodes in Europe (28.4–5.5.2006, 15.8–25.8.2006 and in August2008). These episodes were characterised by: (i) well-identified FRP and TAvalues, and (ii) available ground-based observations of aerosolconcentrations, and optical thickness for the regions where the contributionof the fire smoke to the concentrations of PM2.5 was dominant, incomparison with those of other pollution sources. The emission factors weredetermined separately for the forested and grassland areas; in case ofmixed-type land use, an intermediate scaling was assumed. Despitesignificant differences between the TA and FRP methodologies, an accuratenon-linear fitting was found between the predictions of these approaches.The agreement was comparatively weak only for small fires, for which theaccuracy of both products is expected to be low. The applications of theFire Assimilation System (FAS) in combination with the dispersion modelSILAM showed that both the TA and FRP products are suitable for theevaluation of the emission fluxes from wild-land fires. The fire-originatedconcentrations of aerosols (PM2.5, PM10, sulphates and nitrates)and AOD, as predicted by the SILAM model were mainly within a factor of 2–3compared with the observations. The main challenges of the FAS improvementinclude refining of the emission factors globally, determination of thetypes of fires (smouldering vs flaming), evaluation of the injection heightsof the plumes, and predicting the temporal evolution of fires

An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting