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Determining whether a cloud is composed of spherical water droplets of polyhedral ice crystals (i.e., the thermodynamic phase) from a passive remote sensing instrument is very difficult because of the immense variety of clouds and their highly variable microphysical properties. To improve upon the popular method of radiance ratios, we enhance the classification ability by adding polarimetric sensitivity to an instrument that measures radiance in three short-wave infrared bands. Clouds typically induce a polarization signature on the order of a percent, and so sensitive optics are required for accurate classification. In this paper, we present the combination of spectral and polarimetric sensitivity for cloud thermodynamic phase classification using data from a ground-based, 3-band, short-wave infrared polarimeter and cloud-phase validation from a dual-polarization lidar. We then analyze the classification quality of various methods using surface-fitting techniques to show that the addition of polarimetry is advantageous for cloud classification. 

The sky polarization pattern during solar eclipse totality shifts from the usual daytime clear-sky pattern, with maximum polarization in an arc located 90° from the Sun, to one with maximum polarization slightly above the horizon in a ring nominally concentric about the zenith. A sequence of 9 visible-wavelength all-sky images are shown throughout totality for the 21 August 2017 solar eclipse from a site near Rexburg, ID USA (43.8294°N, 111.8849°W). A neutral region appeared in the southwest quadrant of the all-sky images, directly opposite the eclipsed Sun, and evolved in size and radial position throughout the 2 min 17 s of totality.