Abstract. Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources, and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatio-temporal variability of aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA federated aerosol network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics, and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt), and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites, and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations.
Aerosol Typing Based on Multiwavelength Lidar Observations and Meteorological Model Data