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Cloud properties from both ground-based as well as from geostationary passivesatellite observations have been used previously for diagnosing aerosol–cloudinteractions. In this investigation, a 2-year data set together with fourselected case studies are analyzed with the aim of evaluating the consistencyand limitations of current ground-based and satellite-retrieved cloudproperty data sets. The typically applied adiabatic cloud profile is modifiedusing a sub-adiabatic factor to account for entrainment within the cloud.Based on the adiabatic factor obtained from the combination of ground-basedcloud radar, ceilometer and microwave radiometer, we demonstrate that neitherthe assumption of a completely adiabatic cloud nor the assumption of aconstant sub-adiabatic factor is fulfilled (mean adiabatic factor0.63 ± 0.22). As cloud adiabaticity is required to estimate the clouddroplet number concentration but is not available from passive satelliteobservations, an independent method to estimate the adiabatic factor, andthus the influence of mixing, would be highly desirable for global-scaleanalyses. Considering the radiative effect of a cloud described by thesub-adiabatic model, we focus on cloud optical depth and its sensitivities.Ground-based estimates are here compared vs. cloud optical depth retrievedfrom the Meteosat SEVIRI satellite instrument resulting in a bias of −4 anda root mean square difference of 16. While a synergistic approach based onthe combination of ceilometer, cloud radar and microwave radiometer enablesan estimate of the cloud droplet concentration, it is highly sensitive toradar calibration and to assumptions about the moments of the droplet sizedistribution. Similarly, satellite-based estimates of cloud dropletconcentration are uncertain. We conclude that neither the ground-based norsatellite-based cloud retrievals applied here allow a robust estimate ofcloud droplet concentration, which complicates its use for the study ofaerosol–cloud interactions

Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations