Emissivity and temperature estimation from MSG SEVIRI data; method validation with simulated and NOAA-14 AVHRR data Original Research Article

Land surface temperature (LST), i.e. the radiometric surface temperature corresponding to the field-of-view of a sensor, is an important input for weather forecasting and climate models. Only satellite-based measurements provide the intrinsic spatial averaging required for LST determination on the scale of these models; therefore, LST also provides the best approximation to the thermodynamic temperature at pixel scale. Meteosat Second Generation (MSG) (launched on 28.08.2002), provides data with increased spatial, spectral, and temporal resolutions compared to earlier Meteosats. Among various applications involving data from MSGʹs primary payload, i.e. the Spinning Enhanced Visible and Infrared Imager (SEVIRI), emissivity and LST estimation on an operational basis is of a major concern. Here, issues related to emissivity retrieval from infrared radiances are addressed and the suitability of various algorithms with respect to SEVIRI are discussed. A scheme to estimate SEVIRI channel emissivity based on thermal infrared spectral indices (TISI) is adopted and issues concerning its operational use are addressed. A method validation using synthetic at-ground radiances shows that the achievable accuracy independent of surface type is about 0.005–0.009 for emissivity (in a 11–12 μm channel) and 1.5 K for LST. The method is also applied to NOAA-14 AVHRR data over central Europe. Atmospheric variables were calculated using MODTRAN and ECMWF (European Centre for Medium-range Weather Forecasts) reanalyses.