Combining spectral and polarimetric methods to classify cloud thermodynamic phase

Abstract

Cloud thermodynamic phase--whether a cloud is composed of spherical water droplets or polyhedral ice crystals--is an important parameter for optical communication with space-based instruments, remote sensing of the atmosphere, and, perhaps most importantly, understanding weather and climate. Although some methods exist to detect the phase of clouds, there is still a need for passive remote sensing of cloud thermodynamic phase due to its low-cost, scalability, and ease of use. Two methods for cloud thermodynamic phase classification employ spectral radiance ratios in the short-wave infrared, and the S 1 Stokes parameter, a polarimetric quantity. In this dissertation, the combination of the two methods is realized in an instrument called the short-wave infrared three-channel polarimeter. The coalescence of radiance ratios in the short-wave infrared and polarization channels oriented parallel and perpendicular to the scattering plane provides better classification of cloud phase than either method independently. Despite the improvement, the low-cost system suffered from hardware and software limitations, which caused an increase in noise and polarimetric artifacts. These errors are analyzed and a subset of low-noise data shows even better classification ability. All together, the results attained from the deployment of the polarimeter in early 2019 showed promise that the combination of the two methods is an improvement over past techniques.