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To date, size distributions obtained from the aerosol robotic network (AERONET) havebeen fit with bi-lognormals defined by six secondary microphysicalparameters: the volume concentration, effective radius, and the variance offine and coarse particle modes. However, since the total integrated volumeconcentration is easily calculated and can be used as an accurateconstraint, the problem of fitting the size distribution can be reduced tothat of deducing a single free parameter – the mode separation point. Wepresent a method for determining the mode separation point forequivalent-volume bi-lognormal distributions based on optimization of theroot mean squared error and the coefficient of determination. The extractedsecondary parameters are compared with those provided by AERONET's Level 2.0Version 2 inversion algorithm for a set of benchmark dominant aerosol types,including desert dust, biomass burning aerosol, urban sulphate and seasalt. The total volume concentration constraint is then also lifted byperforming multi-modal fits to the size distribution using nested Gaussianmixture models, and a method is presented for automating the selection of theoptimal number of modes using a stopping condition based on Fisherstatistics and via the application of statistical hypothesis testing. It isfound that the method for optimizing the location of the mode separationpoint is independent of the shape of the aerosol volume size distribution (AVSD), does not require theexistence of a local minimum in the size interval 0.439 μm ≤ <i>r</i> ≤  0.992 μm, and shows some potential for optimizing the bi-lognormalfitting procedure used by AERONET particularly in the case of desert dustaerosol. The AVSD of impure marine aerosol is found to require three modes. Inthis particular case, bi-lognormals fail to recover key features of theAVSD. Fitting the AVSD more generally with multi-modal models allowsautomatic detection of a statistically significant number of aerosol modes,is applicable to a very diverse range of aerosol types, and gives access tothe secondary microphysical parameters of additional modes currently notavailable from bi-lognormal fitting methods

Multi-modal analysis of aerosol robotic network size distributions for remote sensing applications: dominant aerosol type cases