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Stratospheric aerosol particles under non-volcanic conditions are typicallysmaller than 0.1 μm. Due to fundamental limitations of the scatteringtheory in the Rayleigh limit, these tiny particles are hard to measure bysatellite instruments. As a consequence, current estimates of global aerosolproperties retrieved from spectral aerosol extinction measurements tend to be strongly biased.Aerosol surface area densities, for instance, are observed to be about 40%smaller than those derived from correlative in situ measurements (Deshler et al., 2003).An accurate knowledge of the global distribution of aerosol properties is,however, essential to better understand and quantify the role they play inatmospheric chemistry, dynamics, radiation and climate. To address this need a new retrieval algorithm was developed, whichemploys a nonlinear Optimal Estimation (OE) method to iteratively solvefor the monomodal size distribution parameters which are statisticallymost consistent with both the satellite-measured multi-wavelength aerosolextinction data and a priori information. By thus combining spectralextinction measurements (at visible to near infrared wavelengths) withprior knowledge of aerosol properties at background level, even thesmallest particles are taken into account which are practically invisibleto optical remote sensing instruments. The performance of the OE retrieval algorithm was assessed based onsynthetic spectral extinction data generated from both monomodal andsmall-mode-dominant bimodal sulphuric acid aerosol size distributions.For monomodal background aerosol, the new algorithm was shown to fairlyaccurately retrieve the particle sizes and associated integrated properties(surface area and volume densities), even in the presence of large extinctionuncertainty. The associated retrieved uncertainties are a good estimate of the true errors.In the case of bimodal background aerosol, where the retrieved (monomodal)size distributions naturally differ from the correct bimodal values, theassociated surface area (A) and volume densities (V) are, nevertheless,fairly accurately retrieved, except at values larger than1.0 μm2 cm−3 (A) and 0.05 μm3 cm−3(V), where they tend to underestimate the true bimodal values. Due tothe limited information content in the SAGE II spectral extinctionmeasurements this kind of forward model error cannot be avoided here.Nevertheless, the retrieved uncertainties are a good estimate of thetrue errors in the retrieved integrated properties, except where thesurface area density exceeds the 1.0 μm2 cm−3 threshold. When applied to near-global SAGE II satellite extinction measured in1999 the retrieved OE surface area and volume densitiesare observed to be larger by, respectively, 20–50% and 10–40%compared to those estimates obtained by the SAGE~II operational retrievalalgorithm. An examination of the OE algorithm biases with in situ dataindicates that the new OE aerosol property estimates tend to be morerealistic than previous estimates obtained from remotely sensed datathrough other retrieval techniques. Based on the results of this study we therefore suggest that the new Optimal Estimation retrieval algorithmis able to contribute to an advancement in aerosol research by considerablyimproving current estimates of aerosol properties in the lower stratosphere under low aerosol loading conditions

atmospheric chemistry and physics

Optimal estimation retrieval of aerosol microphysical properties from SAGE~II satellite observations in the volcanically unperturbed lower stratosphere