Impacts of small-scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state-of-the-science light-scattering computational capabilities are used to simulate single-scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface roughness range of 0.03--0.15, which is most consistent with direct measurements of scanning electron microscopic images. The degree of surface roughness needs to be accounted for to properly interpret lidar backscattering observations of ice clouds.

Sensitivities of the backscattering properties to microphysical properties (in particular, size and shape) of mineral dust aerosols are examined based on TAMUdust2020, a comprehensive single-scattering property database of irregular aerosol particles. We develop the bulk mineral dust particle models based on size-resolved particle ensembles with randomly distorted shapes and spectrally resolved complex refractive indices, which are constrained with in-situ observations reported in the literature. The lidar ratio is more sensitive to particle shape than particle size, while the depolarization ratio is sensitive to particle size. The simulated bulk backscattering properties (i.e., the lidar ratio and the depolarization ratio) of typical mineral dust particles with effective radii of 0.5–3 µm are reasonably consistent with lidar observations made during several field campaigns. The dust bulk optical property models are applicable to lidar-based remote sensing of dust aerosol properties.