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In order to exploit the full-earth viewing potential of satelliteinstruments to globally characterise aerosols, new algorithms are requiredto deduce key microphysical parameters like the particle size distributionand optical parameters associated with scattering and absorption from spaceremote sensing data. Here, a methodology based on neural networks isdeveloped to retrieve such parameters from satellite inputs and to validatethem with ground-based remote sensing data. For key combinations of inputvariables available from the MODerate resolution Imaging Spectro-radiometer (MODIS) and the Ozone Measuring Instrument (OMI) Level 3 data sets, a grid of 100feed-forward neural network architectures is produced, each having adifferent number of neurons and training proportion. The networks aretrained with principal components accounting for 98% of the variance ofthe inputs together with principal components formed from 38 AErosol RObotic NETwork (AERONET) Level2.0 (Version 2) retrieved parameters as outputs. Daily averaged, co-locatedand synchronous data drawn from a cluster of AERONET sites centred on thepeak of dust extinction in Northern Africa is used for network training andvalidation, and the optimal network architecture for each input parametercombination is identified with reference to the lowest mean squared error.The trained networks are then fed with unseen data at the coastal dust siteDakar to test their simulation performance. A  neural network (NN), trained with co-locatedand synchronous satellite inputs comprising three aerosol optical depthmeasurements at 470, 550 and 660 nm, plus the columnar water vapour (fromMODIS) and the modelled absorption aerosol optical depth at 500 nm (fromOMI), was able to simultaneously retrieve the daily averaged sizedistribution, the coarse mode volume, the imaginary part of the complexrefractive index, and the spectral single scattering albedo – with moderateprecision: correlation coefficients in the range 0.368 ≤ <i>R</i> ≤ 0.514. The network failed to recover the spectral behaviour of the real partof the complex refractive index. This new methodological approach appears tooffer some potential for moderately accurate daily retrieval of the totalvolume concentration of the coarse mode of aerosol at the Saharan dust peakin the area of Northern Africa

Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak