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Satellite instruments are nowadays successfully utilised for measuringatmospheric aerosol in many applications as well as in research. Therefore,there is a growing need for rigorous error characterisation of themeasurements. Here, we introduce a methodology for quantifying theuncertainty in the retrieval of aerosol optical thickness (AOT). Inparticular, we concentrate on two aspects: uncertainty due to aerosolmicrophysical model selection and uncertainty due to imperfect forwardmodelling. We apply the introduced methodology for aerosol optical thicknessretrieval of the Ozone Monitoring Instrument (OMI) on board NASA's EarthObserving System (EOS) Aura satellite, launched in 2004. We apply statisticalmethodologies that improve the uncertainty estimates of the aerosol opticalthickness retrieval by propagating aerosol microphysical model selection andforward model error more realistically. For the microphysical model selectionproblem, we utilise Bayesian model selection and model averaging methods.Gaussian processes are utilised to characterise the smooth systematicdiscrepancies between the measured and modelled reflectances (i.e.residuals). The spectral correlation is composed empirically by exploring aset of residuals. The operational OMI multi-wavelength aerosol retrievalalgorithm OMAERO is used for cloud-free, over-land pixels of the OMIinstrument with the additional Bayesian model selection and model discrepancytechniques introduced here. The method and improved uncertaintycharacterisation is demonstrated by several examples with different aerosolproperties: weakly absorbing aerosols, forest fires over Greece and Russia,and Sahara desert dust. The statistical methodology presented is general; itis not restricted to this particular satellite retrieval application

atmospheric measurement techniques

Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements