Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling

Abstract

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 inAppendix 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). The retrieved thermal admittance values associated with either thermal roughness parameterization were, respectively, 1843 ± 108 J m−2 s−1/2 K−1 and 1926 ± 115 J m−2 s−1/2 K−1.