Retrieval and validation of global, direct, and diffuse irradiance derived from SEVIRI satellite observations. Issue 5 (4th March 2013) - Greuell, W.
Abstract: [1] This paper discusses Surface Insolation under Clear and Cloudy skies derived from SEVIRI imagery (SICCS), a physics‐based, empirically adjusted algorithm developed for estimation of surface solar irradiance from satellite data. Its most important input are a cloud mask product and cloud properties derived from Meteosat/Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations. These observations set the characteristics of the output, namely, a temporal resolution of 15 min, a nadir spatial resolution of 3 × 3 km 2, the period from January 2004 until at least November 2012, and the domain equal to most of the Meteosat disc. SICCS computes global, direct, and diffuse irradiance separately. Direct irradiance for cloudy skies is estimated with an empirical method. Hourly means retrieved with SICCS were validated with data from eight Baseline Surface Radiation Network stations for the year 2006. We found median values of the station biases of +6 W/m 2 (+5%) for direct irradiance, +1 W/m 2 (+1%) for diffuse irradiance, and +7 W/m 2 (+2%) for global irradiance. Replacing the three‐hourly aerosol optical thickness input by monthly means introduces considerable additional biases in the clear‐sky direct (−6%) and diffuse (+26%) irradiances. The performance of SICCS does not degrade when snow covers the surface. Biases do not vary with cloud optical thickness and cloud particle radius. However, the bias in global transmissivity tends to decrease with increasing cloud heterogeneity, and the bias in direct transmissivity is a function of the solar zenith angle. We discuss why satellite retrieval of surface solar irradiance is relatively successful. Key Points: SICCS retrieves irradiance with excellent temporal and spatial resolution. SICCS estimates direct, diffuse, and global irradiance with very good accuracy. The results are relatively insensitive to variations in input variables.