[1] Increasingly, mesoscale meteorological and climate models are used to predict urban weather and climate. Yet, large uncertainties remain regarding values of some urban surface properties. In particular, information concerning urban values for thermal roughness length and thermal admittance is scarce. In this paper, we present a method to estimate values for thermal admittance in combination with an optimal scheme for thermal roughness length, based on METEOSAT-8/SEVIRI thermal infrared imagery in conjunction with a deterministic atmospheric model containing a simple urbanized land surface scheme. Given the spatial resolution of the SEVIRI sensor, the resulting parameter values are applicable at scales of the order of 5 km. As a study case we focused on the city of Paris, for the day of 29 June 2006. Land surface temperature was calculated from SEVIRI thermal radiances using a new split-window algorithm specifically designed to handle urban conditions, as described in Appendix A, including a correction for anisotropy effects. Land surface temperature was also calculated in an ensemble of simulations carried out with the ARPS mesoscale atmospheric model, combining different thermal roughness length parameterizations with a range of thermal admittance values. Particular care was taken to spatially match the simulated land surface temperature with the SEVIRI field of view, using the so-called point spread function of the latter. Using Bayesian inference, the best agreement between simulated and observed land surface temperature was obtained for the Zilitinkevich (1970) and Brutsaert (1975) thermal roughness length parameterizations, the latter with the coefficients obtained by Kanda et al. (2007) Citation: De Ridder, K., C. Bertrand, G. Casanova, and W. Lefebvre (2012), Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling,

The synergy between the Geostationary Earth Radiation Budget (GERB) broadband radiometer and the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) on board the European meteorological satellite Meteosat-8 is exploited to estimate the diurnal variation of the direct short wave aerosols radiative forcing (DSWARF) from biomass burning over Africa at sub-GERB footprint scale. Biomass burning are first identified at the SEVIRI resolution (3 km at nadir) by applying a multispectral thresholding algorithm to the SEVIRI spectral measurements. Reflected SW fluxes at the top-of-atmosphere for smoke aerosols are obtained by converting the measured GERB radiances at the 3x3 SEVIRI pixel window in term of flux using a theoretically derived smoke angular distribution model (ADM) based on the average scene identification from the 3x3 SEVIRI pixel box. The calculated smoke ADM is a function of aerosol optical depth, surface type and solar and viewing geometry. The TOA DSWARF for smoke aerosols is then estimated as the difference between radiative fluxes in the absence and presence of biomass burning aerosols.Finally, the calculated TOA fluxes for smoke aerosols are compared with those obtained without using dedicated smoke ADMs when performing the radiance to flux conversion. This is done to estimate the improvement in the near-real time processing of the GERB and SEVIRI data performed at the Royal Meteorological Institute of Belgium due to the introduction of the smoke ADMs.