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About 70% of the earth’s surface is covered with clouds. They strongly influence the radiation balance and the water cycle of the earth. Hence the detailed monitoring of cloud properties - such as cloud fraction, cloud top temperature, cloud particle size, and cloud water path – is important to understand the role of clouds in the weather and the climate system. The remote sensing with passive sensors is an essential mean for the global observation of the cloud parameters, but is nevertheless challenging. To understand the uncertainty characteristics of cloud remote sensing 12 state-of-art cloud detection and cloud top properties retrievals using SEVIRI observations were inter-compared and validated against CALIPSO and CPR measurements. Our results show that the cloud detection results of the individual algorithms are different for thin cloud layers, broken cloud fields, and aerosol situations. Cloud top height retrievals are uncertain for multilayer situations and thin cloud layers.

Inter-comparison of cloud detection and cloud top height retrievals using the CREW database