Self-organized mapping of aerosol mixtures at aeronet coastal and island sites

Abstract

Satellite ocean-color algorithms generally use aerosol-mixture models to estimate and remove the atmospheric contribution to the measured signal. These models, based on aerosol samples, may or may not be realistic. To investigate the adequacy of the models and ultimately to improve atmospheric correction, we analyze atmospheric optics data collected by the aerosol robotic network project under a wide range of aerosol conditions at coastal and island sites. Using non-supervised classification techniques (probabilistic self-organized mapping), we determine the distribution of retrieved aerosol properties of the total atmospheric column, i.e., the volume size distribution function and the refractive index. The centers of the PRSOM neurons may be used as new aerosols models in radiative transfer algorithms.