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Abstract. This paper investigates a potential of two remotely sensed wild-land fire characteristics: 4-µm Brightness Temperature Anomaly (TA) and Fire Radiative Power (FRP) for the needs of operational chemical transport modelling and short-term forecasting of atmospheric composition and air quality. The treatments of the TA and FRP data are presented and a methodology for evaluating the emission fluxes of primary aerosols (PM 2.5 and total PM) is described. The method does not include the complicated analysis of vegetation state, fuel load, burning efficiency and related factors, which are uncertain but inevitably involved in approaches based on burnt-area scars or similar products. The core of the current methodology is based on the empirical emission factors that are used to convert the observed temperature anomalies and fire radiative powers into emission fluxes. These factors have been derived from the analysis of several fire episodes in Europe (28.4-5.5.2006, 15.8-25.8.2006 and in August 2008. These episodes were characterised by: (i) well-identified FRP and TA values, and (ii) available groundbased observations of aerosol concentrations, and optical thickness for the regions where the contribution of the fire smoke to the concentrations of PM 2.5 was dominant, in comparison with those of other pollution sources. The emission factors were determined separately for the forested and grassland areas; in case of mixed-type land use, an intermediate scaling was assumed. Despite significant differences between the TA and FRP methodologies, an accurate nonlinear fitting was found between the predictions of these approaches. The agreement was comparatively weak only for small fires, for which the accuracy of both products is exCorrespondence to: M. Sofiev (mikhail.sofiev@fmi.fi) pected to be low. The applications of the Fire Assimilation System (FAS) in combination with the dispersion model SILAM showed that both the TA and FRP products are suitable for the evaluation of the emission fluxes from wild-land fires. The fire-originated concentrations of aerosols (PM 2.5 , PM 10 , sulphates and nitrates) and AOD, as predicted by the SILAM model were mainly within a factor of 2-3 compared with the observations. The main challenges of the FAS improvement include refining of the emission factors globally, determination of the types of fires (smouldering vs flaming), evaluation of the injection heights of the plumes, and predicting the temporal evolution of fires.

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m 2 . With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were OPEN ACCESSRemote Sens. 2012, 4 1191 fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level.

Identification of aerosol types is important, because different aerosol types have different effects on health, visibility and climate change. The space‐borne lidar CALIOP on‐board the CALIPSO satellite provides information on the types of aerosols in the separate layers that can be detected with this instrument. In this study, the aerosol subtypes of CALIOP measurements are compared with daily aerosol types derived from AERONET level 2.0 inversion data. AERONET aerosol types are categorized by single scattering albedo and Ångström exponent values. The comparison shows that 70% of the CALIOP and AERONET aerosol types are in agreement. Best agreement is achieved for dust and polluted dust types.

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