A database management system has been realized that, by taking physical and chemical properties (the complex refractive index and the size distribution) of basic components as its starting point, allows the user to obtain optical properties of default as well as user-defined aerosol classes. Default classes are defined in accordance with the most widely known and used aerosol models. We obtain user-defined classes by varying the mixing ratio of components, creating new mixtures of default components, or by defining user components, thereby supplying the size distribution and the refractive index. The effect of relative humidity (RH) on the refractive index and the size distribution is properly accounted for up to RH = 99%. The two known mechanisms of obtaining classes from components are allowed (internal or external mixing).

Abstract. Satellite monitoring of aerosol properties using passive techniques is widely considered a crucial tool for the study of climatic effects of atmospheric particulate [Kaufman et al., 1997]. Only space-based observations can provide the required global coverage information on spatial distribution and temporal variation of the aerosol field. This paper describes a method for deriving aerosol optical thickness at 500 nm and aerosol type from Global Ozone Monitoring Experiment (GOME) data over the ocean under cloud-free conditions. GOME, flying on board the second European Remote Sensing satellite (ERS 2) since April 1995, is a spectrometer that measures radiation reflected from Earth in the spectral range 240-793 nm. The features of the instrument relevant to the aerosol retrieval task are its high relative radiometric accuracy (better than 1%), its spectral coverage, and its spectral resolution, which allows wavelengths in spectral regions free of molecular absorption (atmospheric windows) to be selected. The method presented is based on a pseudo-inversion approach in which measured reflectance spectra are fitted to simulated equivalents computed using a suitable radiative transfer model. The crucial aspects of this method are the high accuracy and the nonapproximate nature of the radiative transfer model, which simulates the spectra during the fitting procedure, and careful selection of candidate aerosol classes. A test application of the proposed method to a Saharan dust outbreak which occurred in June 1997 is presented, showing that in spite of the instrument's low spatial resolution, information on both optical thickness and spectral characterization of the aerosol can be retrieved from GOME data. Preliminary comparisons of the results with independent estimates of the aerosol content show that a good correlation exists, encouraging planning of a systematic application of the method.

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Abstract. The identification of precipitation areas by microwave based rain algorithms can be improved by means of cloud classification schemes based on multispectral observations. Several recent studies have demonstrated the potential of cloud microphysical and optical characterization for the improvement of passive microwave rain estimates, especially in detecting likely precipitating pixels over land. The multispectral sensing capabilities of MODIS onboard Aqua are exploited to characterize the cloudy scenario, using a twofold approach: a) an RGB technique to qualitatively identify the different cloud systems on the basis of the combination of radiances measured in three selected channels, and b) a quantitative description of cloud top in terms of optical thickness (τ), effective radius (Re) and top temperature (Tc). The information gathered by the multispectral analysis of the cloud field from MODIS is contrasted with the rain intensity at the ground as derived from the AMSR-E operational algorithm, to assess the statistical relationships between microphysical parameters and the rain intensity for such nearly simultaneous and co-located observations.