Enhancing lidar aerosol typing schemes: a lidar/photometer synergy

In this study we present a synergistic approach between lidar and photometer to separate volcanic ash and desert dust and, ultimately, to enhance lidar-based aerosol typing schemes. Typically, the lidar depolarization ratio measurements can be used to distinguish dust and ash with ash depolarization ratio reaching higher values. However, the variability of aerosol depolarization ratio makes it difficult to use it in automatic typing techniques. The imaginary part of refractive index when using in situ data shows stronger absorption than mineral dust; therefore, here, we make use of microphysical AERONET data to define the two aerosol classes (i.e., ash/dust). Then, trivariate Mahalanobis distance is estimated based on the real and imaginary parts of the refractive index and the single scattering albedo for any given AERONET measurement and the type is assigned. This information is then passed on in lidar aerosol typing algorithms and the aerosol type is allocated in the vertical dimension. The methodology is applied to the Potenza ACTRIS site in Italy during an intense desert dust event where an AERONET photometer and an ACTRIS lidar are collocated.