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[1] The rotating shadowband spectroradiometer (RSS) is a high-resolution device that measures the total, direct, and diffuse intensity of sunlight at 1016 different wavelengths. RSS data can be used to retrieve gas amounts and aerosol properties as well as to assess the accuracy of retrievals using data from lower-spectral resolution instruments, such as the multifilter rotating shadowband radiometer (MFRSR). An algorithm to retrieve aerosol and gas amounts and the aerosol size distribution from RSS data, using the full resolution and using selected wavelengths, has been developed (Gianelli, 2004). The results of the retrievals, applied to RSS data from the southern Great Plains (SGP) site, indicate a number of things about the aerosol size distribution and our ability to retrieve aerosol information accurately. First, we show that the aerosol size distribution at SGP is at least bimodal. The notion that a more complex size distribution can be modeled with an appropriately selected monomodal distribution produces an unacceptable fit to the data once the separation of aerosol extinction from nitrogen dioxide absorption at short wavelengths is taken into consideration. Second, we find that the amount of retrievable aerosol information is limited by the wavelength range of the data. This is exemplified by the indeterminacy of the coarse-mode effective radius and the interdependence in the retrievals of the fine-mode effective radius and effective variance. Third, when the fine-mode effective variance is constrained, an annual cycle in fine-mode effective radius values emerges from the retrieval results, with a maximum in March and a minimum in September. Conceivably, changes in the effective variance could influence the observed pattern as well. The large margin of error in the coarse-mode effective radius leads to relatively small error bars for the coarse-and fine-mode optical depths, the fine-mode effective radius, and ozone, except on those days when the coarse-mode optical depth is high. Examining the retrieval results for different wavelength combinations of five RSS channels allows us to investigate whether or not the MFRSR channels are optimized to retrieve aerosol information or if a different filter set would increase the robustness of the retrievals. We show that replacing the 670 nm channel with one at either 375 or 1034 nm improves the retrieval accuracy. In particular, retrievals with the 1034 nm channel very closely reproduce the full RSS retrieval results, provided that the NO 2 column amount can be determined by other means. The precision of the retrieved values of the fine-mode effective radius is shown to be strongly sensitive to the precision of measured NO 2 amounts; a margin of error in column NO 2 of 0.3 Dobson units results in a corresponding margin of error of 0.04 mm in the fine-mode effective radius. This confirms that aerosol sizes cannot be inferred accurately from optical depth spectra alone if either the amount of NO 2 above a site is unknown or an inaccurate value is assumed.Citation...

[1] Empirical Orthogonal Function (EOF) analysis is performed on ground-based shadowband radiometer and Sun photometer data. The data come from Multifilter Rotating Shadowband Radiometer (MFRSR) instruments located at the central and extended facilities of the Southern Great Plains (SGP) research site of the Department of Energy's Atmospheric Radiation Measurement (ARM) program, as well as the Rotating Shadowband Spectroradiometer (RSS) 102 and 105 and the CIMEL Sun photometer CSPHOT located at the central facility at SGP. The EOFs show the variability in total aerosol optical depth and provide some qualitative information on the separation of the coarse and fine aerosol modes. In addition, as has been demonstrated previously with satellite data, EOF analysis also exposes several flaws and inconsistencies within the ground-based data sets. These inhomogeneities include optical depth anomalies in some MFRSR filters, wavelength shifts in the RSS, and problems with the data transmitter of the CIMEL instrument. Therefore EOF analysis is shown to be a quick and effective means not only of assessing the general aerosol behavior in the air above a particular monitoring instrument, but also of identifying both known and unanticipated influences on the data coming from within the instrument itself.