Estimation of Aerosol Extinction-to-Backscatter Ratios using AERONET Measurements and Cluster Analysis

Radiance measurements and inversions of the AErosol RObotic NETwork (AERONET) are used to characterize global atmospheric aerosols and key parameters such as the phase functions and column extinction-to-backscatter ratio (lidar ratio, Sa) for the analysis of lidar measurements. This study uses more than 10 records of aerosol size distributions and complex refractive indices to generate several optical properties of the aerosol at more 200 sites worldwide. These properties together with the radiance measurements are then classified using classical clustering methods which group the sites according to the type of aerosol with the greatest frequency of occurrence at that site. Six significant clusters are identified: two types of desert dust, biomass burning, polluted continental, clean continental, and a dust+smoke aerosol. In addition to categorizing the aerosol types, all the column properties that are important for the analysis and validation of lidar measurements such as the lidar ratio, optical depth and phase functions are determined for each of the seven aerosol types. The variances of the aerosol determined using principle component analysis (PCA). Aerosol lidar ratios at 532 nm of the six categories of aerosol are calculated. INTRODUCTION The extinction/backscatter ratio (Sa) is an important parameter used in the determination of the aerosol extinction and subsequently the optical depth from lidar backscatter measurements. Historically, the use of Sa came into common use because the single scattering lidar equation is underdetermined. A unique solution is only possible if the two unknowns, extinction and backscatter are combined into one variable (Sa). There is therefore a need to determine Sa at various locations worldwide especially in light of the advent of satellite-based lidar measurements such as the Cloud and Aerosol Lidar and Infrared imager Pathfinder Spaceborne Observations (CALIPSO). This study shows that assigning a lidar ratio to a given location based on a long-term average is unrealistic. This is because at any given location, the aerosol type is highly variable on time scales as short as a few hours. These variations result from transport of distinct airmasses to a site and non-systematic events such as fires, wind gusts, hurricanes, tornadoes, and land clearing and development activities. These variations lead to diverse aerosol characteristics at each site on time scales of a few hours and preclude the long term averaging of aerosols to develop a representative set of characteristics for a site or region. Aerosol optical measurements must therefore be made at short time scales (about 30 minutes) to develop a large data base which can be used to derive statistically significant correlations. The AErosol RObotic NETwork (AERONET) measurements are likely to provide such a data base albeit for column rather than vertically-resolved measurements. AERONET is an automatic robotic Sun and sky scanning measurement network that has grown rapidly to over 200 sites worldwide. AERONET uses multi-angle radiance measurements to retrieve the discrete aerosol size distributions in 22 size bins ranging from 0.05 to 15 μm and the complex refractive index. This study uses the AERONET refractive indices and size distributions for Mie scattering calculations from which several parameters of aerosols are determined. The calculated single scattering albedos determined using Mie calculations are compared with those of AERONET retrievals for code validation. On average , the relative error between these two results is less than 1%. Cluster analysis is used for categorization of atmospheric aerosol types. Six significant types: two types of desert dust, biomass burning, polluted continental, clean continental, and a dust+smoke aerosol are suggested by the cluster analysis.  Seoul National University, Seoul, S. Korea