[1] As a fast developing country covering a large territory, China is experiencing rapid environmental changes. High concentrations of aerosols with diverse properties are emitted in the region, providing a unique opportunity for understanding the impact of environmental changes on climate. Until very recently, few observational studies were conducted in the source regions. The East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE) attempts to characterize the physical, optical and chemical properties of the aerosols and their effects on climate over China. This study presents some preliminary results using continuous high-quality measurements of aerosol, cloud and radiative quantities made at the first EAST-AIRE baseline station at Xianghe, about 70 km east of Beijing over a period of one year (September 2004to September 2005. It was found that the region is often covered by a thick layer of haze (with a yearly mean aerosol optical depth equal to 0.82 at 500 nm and maximum greater than 4) due primarily to anthropogenic emissions. An abrupt ''cleanup'' of the haze often took place in a matter of one day or less because of the passage of cold fronts. The mean single scattering albedo is approximately 0.9 but has strong day-to-day variations with maximum monthly averages occurring during the summer. Large aerosol loading and strong absorption lead to a very large aerosol radiative effect at the surface (the annual 24-hour mean values equals 24 W m À2 ), but a much smaller aerosol radiative effect at the top of the atmosphere (one tenth of the surface value). The boundary atmosphere is thus heated dramatically during the daytime, which may affect atmospheric stability and cloud formation. In comparison, the cloud radiative effect at the surface is only moderately higher (À41 W m À2 ) than the aerosol radiative effect at the surface.

[1] Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been used to address aerosol climatic issues in many parts of the world, but their quality has yet to be determined over China. This paper presents a thorough evaluation of aerosol optical depth (AOD) data retrieved from MODIS collections 4 (C004) and 5 (C005) at two AERONET sites in northern and southeastern China. Established under the aegis of the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE) project, the two sites, Xianghe and Taihu, have distinct ecosystems and climate regimes, resulting in differences in retrieval performance. At the rural northeastern site (Xianghe), MODIS C004 retrievals generally overestimate AOD at 550 nm during clean days, with the largest errors occurring during winter. In the warm and humid regions of southeastern China (Taihu), MODIS C004 retrievals overestimate AOD throughout the year. The systematic error at Xianghe is primarily due to the fixed surface reflectance ratio, while as the error at Taihu is mainly caused by the choice of the single scattering albedo (SSA) for the fine model aerosols. Both problems are alleviated considerably in the C005. The comparisons between C005 retrievals and AERONET data show much higher correlation coefficient, lower offset and a slope closer to unity. Also, the variability of AOD retrieval among neighboring pixels is reduced by several factors. The strong overestimation problem at small AOD values was fixed by using dynamic reflectance ratios that vary with the vegetation index and scattering angle. However, significant uncertainties remain because of the use of highly simplified aerosol models.

Abstract-Operational algorithms for retrieval of aerosols from satellite observations are typically created manually based on the domain knowledge. Validation studies, where the retrievals are compared to the available ground-truth data, are periodically performed with the goal of understanding how to further improve the quality of the retrieval algorithms. This letter describes a data-mining approach aimed to facilitate this highly laborintensive process. It is based on training a neural network for retrieval and comparing its performance with that of the operational algorithm. The situations, where a neural network is more accurate, point to the weaknesses of the operational algorithm that could be corrected. Use of decision trees is proposed to provide easily interpretable descriptions of such situations. The approach was applied on 3646 collocated Moderate Resolution Imaging Spectroradiometer and AERONET observations over the continental U.S. related to the retrieval of aerosol optical thickness. The experiments showed that the approach is feasible and that it can be a valuable tool for the domain scientists working on the development of retrieval algorithms.

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