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Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gasalgorithms make use of climatological surface reflectivity databases. Forexample, cloud and NO2 retrievals for the Ozone Monitoring Instrument(OMI) use monthly gridded surface reflectivity climatologies that do notdepend upon the observation geometry. In reality, reflection of incomingdirect and diffuse solar light from land or ocean surfaces is sensitive tothe sun–sensor geometry. This dependence is described by the bidirectionalreflectance distribution function (BRDF). To account for the BRDF, we proposeto use a new concept of geometry-dependent Lambertian equivalent reflectivity(LER). Implementation within the existing OMI cloud and NO2 retrievalinfrastructure requires changes only to the input surface reflectivitydatabase. The geometry-dependent LER is calculated using a vector radiativetransfer model with high spatial resolution BRDF information from theModerate Resolution Imaging Spectroradiometer (MODIS) over land and theCox–Munk slope distribution over ocean with a contribution fromwater-leaving radiance. We compare the geometry-dependent and climatologicalLERs for two wavelengths, 354 and 466 nm, that are used in OMI cloudalgorithms to derive cloud fractions. A detailed comparison of the cloudfractions and pressures derived with climatological and geometry-dependentLERs is carried out. Geometry-dependent LER and corresponding retrieved cloudproducts are then used as inputs to our OMI NO2 algorithm. We findthat replacing the climatological OMI-based LERs with geometry-dependent LERscan increase NO2 vertical columns by up to 50 % in highlypolluted areas; the differences include both BRDF effects and biases betweenthe MODIS and OMI-based surface reflectance data sets. Only minor changes toNO2 columns (within 5 %) are found over unpolluted and overcastareas

atmospheric measurement techniques

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms