Quantitative Three-Dimensional Ice Roughness From Scanning Electron Microscopy

Light-scattering properties of ice crystals in cirrus clouds can be greatly influenced by the surface roughness of these crystals, but assumptions about such roughening have so far not been well constrained by direct observations. Here, we present a method for inferring three-dimensional representations of surface morphology of hexagonal ice crystals grown in a scanning electron microscope. These representations are obtained at approximately micrometer resolution using Gauss-Newton inversion within a Bayesian framework. Statistical analysis of the resulting datasets permits characterization of ice surface roughness with a much higher statistical confidence than previously possible. A survey of results between -39^oC to -29^oC shows that statistical measures of roughness are sensitive not only to the degree of roughening, but also to the symmetry of the roughening. These results suggest that remote sensing and in situ measurements of atmospheric ice clouds could yield more information than has previously been appreciated.