An improved algorithm of cloud droplet size distribution from POLDER polarized measurements

Abstract

Abstract The Polarization and Directionality of Earth Reflectances (POLDER) instrument provides unique cloud droplet radius (CDR) and effective variance (EV) observations for the analysis of clouds on the global scale. However, the cloud droplet size distribution estimated using the conventional POLDER algorithm is limited by its coarse spatial resolution (150\u202fkm) and insufficient information for large droplets (CDR\u202f>\u202f15\u202fμm). In this study, we proposed an improved primary cloudbow retrieval (PCR) algorithm to estimate CDR and EV from POLDER. Simulated retrievals based on a radiative transfer model indicate that primary cloudbow measurements are sensitive to large droplets (CDR\u202f>\u202f15\u202fμm) and enable the retrieval to be applied at a higher spatial resolution; therefore, we employ POLDER polarized measurements from both primary and supernumerary cloudbow regions in the PCR algorithm. Retrieval cases using POLDER measurements reveal that the PCR algorithm is robust when the cloud fields are homogeneous. When the cloud field is heterogeneous, the estimation of CDR is sensitive to the scattering angle ranges as well as the grid size, with uncertainty