1Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, 82234 Wessling, Germany
2Meteorologisches Institut, Ludwig-Maximilians-Universität, Theresienstrasse 37, 80333 München, Germany
3Deutscher Wetterdienst, Meteorologisches Observatorium Hohenpeissenberg, Albin-Schwaiger-Weg 10, 82383 Hohenpeissenberg, Germany
4Deutscher Wetterdienst, Department Climate Monitoring, Satellite Application Facility on Climate Monitoring, Kaiserleistrasse 35, 63067 Offenbach am Main, Germany
5Institute of Environmental Physics, University of Bremen, FB1, Otto-Hahn-Allee 1, 28334 Bremen, Germany
Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 79%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and underestimated by the CMSAF code (−16.4 K with a standard deviation of 37.3 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation for the DLR and to overestimation for the CMSAF algorithm. Cloud effective radii are most difficult to evaluate and not always the algorithms are able to produce realistic values. The CMSAF cloud water path, which is a combination of the last two quantities, is particularly accurate for ice clouds, while water clouds are overestimated, mainly because of the effective radius overestimation for water clouds.