Journal of Geophysical Research: Atmospheres

Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements

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Abstract

[1] We use radiance measurements and inversions of the Aerosol Robotic Network (AERONET) (Dubovik and King, 2000; Holben et al., 1998; Holben et al., 2001) to classify global atmospheric aerosols using the complete archive of the AERONET data set as of December 2002 and dating back to 1993 for some sites. More than 143,000 records of AERONET solar radiance measurements, derived aerosol size distributions, and complex refractive indices are used to generate the optical properties of the aerosol at more than 250 sites worldwide. Each record is used in a clustering algorithm as an object, with 26 variables comprising both microphysical and optical properties to obtain six significant clusters. Using the mean values of the optical and microphysical properties together with the geographic locations, we identified these clusters as desert dust, biomass burning, urban industrial pollution, rural background, polluted marine, and dirty pollution. When the records in each cluster are subdivided by optical depth class, the trends of the class size distributions show that the extensive properties (mode amplitude and total volume) vary by optical depth, while the intensive properties (mean radius and standard deviation) are relatively constant. Seasonal variations of aerosol types are consistent with observed trends. In particular, the periods of intense biomass burning activity and desert dust generation can be discerned from the data and the results of the analyses. Sensitivity and uncertainty analyses show that the clustering algorithm is quite robust. When subsets of the data set are randomly created and the clustering algorithm applied, we found that more than 94% of the records retain their classification. Adding 10% random noise to the microphysical properties and propagating this error through the scattering calculations, followed by the clustering algorithm, results in a misclassification rate of less than 9% when compared with the noise-free data.

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