Cloud droplet effective radius (CER) and number concentration (CDNC) are two critical microphysical properties of liquid water clouds, which play essential roles in the Earth’s radiative energy balance and atmospheric hydrological cycle. Even though many satellite remote sensing techniques have been developed to obtain these two properties, the observations are often limited to the daytime. In this study, a method to estimate CER and CDNC of liquid water clouds over global ocean area during both daytime and nighttime from CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) measurements is presented. The size sensitivity of the dual-wavelength (532 nm & 1064 nm) layer-integrated attenuated backscattering signals from CALIOP is checked and information content for liquid water cloud CER retrieval is found. Taking use of the artificial neural network (ANN) technique, the CER and then the CDNC are estimated from CALIOP by combining the polarization ratio and the dual wavelength attenuated backscattering signals. The comparisons with CER and CDNC estimated from MODIS (Moderate Resolution Imaging Spectroradiometer) during daytime demonstrate the feasibility of this new method. Both the daytime and nighttime CER and CDNC derived from CALIOP observations are presented in this paper and the day-night variation of liquid water cloud is discussed which would provide useful day-night variation of liquid water cloud properties.