Abstract
Abstract. Cloud droplet number concentration (CDNC) is an important microphysical property of liquid clouds that impacts radiative forcing, precipitation and is pivotal for understanding cloud–aerosol interactions. Current studies of this parameter at global scales with satellite observations are still challenging, especially because retrieval algorithms developed for passive sensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua) have to rely on the assumption of cloud adiabatic growth. The active sensor component of the A-Train constellation (i.e., Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC from depolarization measurements at 532 nm. For such a 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 Polarization and Directionality of the Earth Reflectance (POLDER)/PARASOL (two passive sensors, components of the A-Train) are used to constrain CDNC retrievals from CALIOP. Intercomparison of CDNC products retrieved from MODIS and CALIOP sensors is performed, and the impacts of cloud entrainment, drizzling, horizontal heterogeneity and effective radius 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 can contribute to future studies of global cloud–aerosol–precipitation interaction and parameterization of clouds in global climate models (GCMs).
Highlights
Cloud droplet number concentration is one of the most important cloud microphysical properties as it is intimately related to the cloud droplet size distribution, chemical composition of condensation nucleation nuclei (CCN), and the thermodynamical and dynamical state of the cloudy air during its formation (Seinfeld and Pandis, 1998)
In addition to the MODIS re, we investigated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Cloud droplet number concentration (CDNC) retrievals using the product of the re and the effective variance of size distribution derived from POLDER3/PARASOL
In this paper we examined their geographical distributions and seasonal variations with the MODIS and CALIOP observations
Summary
Cloud droplet number concentration is one of the most important cloud microphysical properties as it is intimately related to the cloud droplet size distribution, chemical composition of condensation nucleation nuclei (CCN), and the thermodynamical and dynamical state (i.e., updraft velocity, mixing rates) of the cloudy air during its formation (Seinfeld and Pandis, 1998). This property is directly linked to cloud evolution (i.e., water vapor condensation, droplet nucleation and drizzling processes), impacts cloud radiative properties and precipitation development, and it is pivotal in cloud– aerosol interactions. Validation of relationships between cloud microphysics and marine biogenic aerosols that serve as CCN
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