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Cloud droplet number concentration (CDNC) is an important microphysicalproperty of liquid clouds that impacts radiative forcing, precipitation andis pivotal for understanding cloud–aerosol interactions. Current studiesof this parameter at global scales with satellite observations are stillchallenging, especially because retrieval algorithms developed for passivesensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua)have to rely on the assumption of cloud adiabatic growth. The active sensorcomponent of the A-Train constellation (i.e., Cloud-Aerosol Lidar withOrthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC fromdepolarization measurements at 532 nm. For such a case, the retrieval does notrely on the adiabatic assumption but instead must use a priori informationon effective radius (re), which can be obtained from other passivesensors. In this paper, re values obtained from MODIS/Aqua and Polarization andDirectionality of the Earth Reflectance (POLDER)/PARASOL (two passivesensors, components of the A-Train) are used to constrain CDNC retrievalsfrom CALIOP. Intercomparison of CDNC products retrieved from MODIS andCALIOP sensors is performed, and the impacts of cloud entrainment,drizzling, horizontal heterogeneity and effective radius are discussed. Byanalyzing the strengths and weaknesses of different retrieval techniques,this study aims to better understand global CDNC distribution andeventually determine cloud structure and atmospheric conditions in whichthey develop. The improved understanding of CDNC can contribute to futurestudies of global cloud–aerosol–precipitation interaction andparameterization of clouds in global climate models (GCMs)

Study of global cloud droplet number concentration with A-Train satellites