Determination of circumsolar radiation from Meteosat Second Generation

Abstract

Reliable data on circumsolar radiation, which is caused by scattering of sunlight by cloud or aerosol particles, is becoming more and more important for the resource assessment and design of concentrating solar technologies (CSTs). However, measuring circumsolar radiation is demanding and only very limited data sets are available. As a step to bridge this gap, a method was developed which allows for determination of circumsolar radiation from cirrus cloud properties retrieved by the geostationary satellites of the Meteosat Second Generation (MSG) family. The method takes output from the COCS algorithm to generate a cirrus mask from MSG data and then uses the retrieval algorithm APICS to obtain the optical thickness and the effective radius of the detected cirrus, which in turn are used to determine the circumsolar radiation from a pre-calculated look-up table. The look-up table was generated from extensive calculations using a specifically adjusted version of the Monte Carlo radiative transfer model MYSTIC and by developing a fast yet precise parameterization. APICS was also improved such that it determines the surface albedo, which is needed for the cloud property retrieval, in a self-consistent way instead of using external data. Furthermore, it was extended to consider new ice particle shapes to allow for an uncertainty analysis concerning this parameter. We found that the nescience of the ice particle shape leads to an uncertainty of up to 50%. A validation with 1 yr of ground-based measurements shows, however, that the frequency distribution of the circumsolar radiation can be well characterized with typical ice particle shape mixtures, which feature either smooth or severely roughened particle surfaces. However, when comparing instantaneous values, timing and amplitude errors become evident. For the circumsolar ratio (CSR) this is reflected in a mean absolute deviation (MAD) of 0.11 for both employed particle shape mixtures, and a bias of 4 and 11%, for the mixture with smooth and roughend particles, respectively. If measurements with sub-scale cumulus clouds within the relevant satellite pixels are manually excluded, the instantaneous agreement between satellite and ground measurements improves. For a 2-monthly time series, for which a manual screening of all-sky images was performed, MAD values of 0.08 and 0.07 were obtained for the two employed ice particle mixtures, respectively.