Global cloud droplet number concentration observed with combined use of A-Train satellites

Cloud droplet number concentration (CDNC) is an important microphysical property of liquid clouds that impacts radiative forcing, precipitation and interacts with aerosols. Remote sensing of this parameter at global scales from satellites are still challenging, especially because retrieval algorithms developed for passive sensors (i.e. MODIS/Aqua) have to rely on the assumption of cloud adiabatic growth. The active sensor CALIOP/CALIPSO allows retrievals of CDNC from depolarization measurements at 532 nm. For that case, the retrieval does not rely on the adiabatic assumption but instead must use a priori information on effective radius (re), which can be obtained from other passive sensors. In this paper, re values obtained from MODIS/Aqua and POLDER/PARASOL are used to constrain CDNC retrievals from CALIOP. Intercomparison of CDNC products retrieved from MODIS and CALIOP sensors is performed, and their differences are discussed. By analyzing the strengths and weaknesses of different retrieval techniques, this study aims to better understand global CDNC distribution, and eventually determine cloud structure and atmospheric conditions in which they develop. The improved understanding of CDNC would help for the future studies of global cloud-aerosol-precipitation interactions.