Dual-Wavelength Polarimetric Lidar Observations of the Volcanic Ash Cloud Produced during the 2016 Etna Eruption

Lidar observations are very useful to analyse dispersed volcanic clouds in the troposphere
mainly because of their high range resolution, providing morphological as well as microphysical
(size and mass) properties. In this work, we analyse the volcanic cloud of 18 May 2016 at Mt.
Etna, in Italy, retrieved by polarimetric dual-wavelength Lidar measurements. We use the AMPLE
(Aerosol Multi-Wavelength Polarization Lidar Experiment) system, located in Catania, about 25 km
from the Etna summit craters, pointing at a thin volcanic cloud layer, clearly visible and dispersed
from the summit craters at the altitude between 2 and 4 km and 6 and 7 km above the sea level.
Both the backscattering and linear depolarization profiles at 355 nm (UV, ultraviolet) and 532 nm
(VIS, visible) wavelengths, respectively, were obtained using different angles at 20◦
, 30◦
, 40◦ and
90◦
. The proposed approach inverts the Lidar measurements with a physically based inversion
methodology named Volcanic Ash Lidar Retrieval (VALR), based on Maximum-Likelihood (ML).
VALRML can provide estimates of volcanic ash mean size and mass concentration at a resolution of
few tens of meters. We also compared those results with two methods: Single-variate Regression
(SR) and Multi-variate Regression (MR). SR uses the backscattering coefficient or backscattering and
depolarization coefficients of one wavelength (UV or VIS in our cases). The MR method uses the
backscattering coefficient of both wavelengths (UV and VIS). In absence of in situ airborne validation
data, the discrepancy among the different retrieval techniques is estimated with respect to the VALR
ML algorithm. The VALR ML analysis provides ash concentrations between about 0.1 µg/m3 and
1 mg/m3 and particle mean sizes of 0.1 µm and 6 µm, respectively. Results show that, for the SR
method differences are less than <10%, using the backscattering coefficient only and backscattering
and depolarization coefficients. Moreover, we find differences of 20–30% respect to VALR ML,
considering well-known parametric retrieval methods. VALR algorithms show how a physics-based
inversion approaches can effectively exploit the spectral-polarimetric Lidar AMPLE capability.