Simulations and experimental results of cloud thermodynamic phase classification with three SWIR spectral bands

Abstract

© The Authors. Knowing the thermodynamic phase of a cloud-whether it is composed of spherical water droplets or polyhedral ice crystals-is critical in remote sensing applications and in climate studies. Liquid water and ice have different absorptive properties in certain spectral bands that can be exploited to identify the phase of clouds using ground-based, passive remote sensing. Our simulations found that ground-based radiance measurements at three spectral channels (1.55, 1.64, and 1.70 μm) provide improved discrimination when analyzed in three spectral dimensions as opposed to previous approaches based in two-dimensional parameter space. Our simulations show that these bands provide good discrimination between liquid-water and ice clouds when the optical depth is large. We also show measurements from a ground-based spectrometer confirming the cloud-phase sensing ability of these three channels, with validation provided by a dual-polarization lidar system.