Maximum-Likelihood Retrieval of Volcanic Ash Concentration and Particle Size From Ground-Based Scanning Lidar

Abstract

An inversion methodology, named maximum-likelihood (ML) volcanic ash light detection and ranging (Lidar) retrieval (VALR-ML), has been developed and applied to estimate volcanic ash particle size and ash mass concentration within volcanic plumes. Both estimations are based on the ML approach, trained by a polarimetric backscattering forward model coupled with a Monte Carlo ash microphysical model. The VALR-ML approach is applied to Lidar backscattering and depolarization profiles, measured at visible wavelength during two eruptions of Mt. Etna, Catania, Italy, in 2010 and 2011. The results are compared with those of ash products derived from other parametric retrieval algorithms. A detailed comparison among these different retrieval techniques highlights the potential of VALR-ML to determine, on the basis of a physically consistent approach, the ash cloud area that must be interdicted to flight operations. Moreover, the results confirm the usefulness of operating scanning Lidars near active volcanic vents.