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One of four main focus areas of the PEEX\udinitiative is to establish and sustain long-term, continuous, and\udcomprehensive ground-based, airborne, and seaborne observation infrastructure\udtogether with satellite data. The Advanced Along-Track Scanning Radiometer\ud(AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR\uddata can be used to retrieve aerosol optical depth (AOD) over both land and\udocean, which is an important parameter in the characterization of aerosol\udproperties. In recent years, aerosol retrieval algorithms have been developed\udboth over land and ocean, taking advantage of the features of dual view,\udwhich can help eliminate the contribution of Earth's surface to\udtop-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of\udthe Climate Change Initiative (CCI), provides users with three AOD retrieval\udalgorithms for AATSR data, including the Swansea algorithm (SU), the\udATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL\udRetrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the\udAerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and\udAE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin\udevaluation using the validation tool of the AeroCOM (Aerosol Comparison\udbetween Observations and Models) project. For the purpose of evaluating\uddifferent performances of these three algorithms in calculating AODs over\udmainland China, we introduce ground-based data from CARSNET (China Aerosol\udRemote Sensing Network), which was designed for aerosol observations in\udChina. Because China is vast in territory and has great differences in terms\udof land surfaces, the combination of the AERONET and CARSNET data can\udvalidate the L2 AOD products more comprehensively. The validation results\udshow different performances of these products in 2007, 2008, and 2010. The SU\udalgorithm performs very well over sites with different surface conditions in\udmainland China from March to October, but it slightly underestimates AOD over\udbarren or sparsely vegetated surfaces in western China, with mean bias error\ud(MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with\uda low root mean square error (RMSE) smaller than 0.2 over most sites and the\udsame error distribution as the SU product. The main limits of the ADV\udalgorithm are underestimation and applicability; underestimation is\udparticularly obvious over the sites of Datong, Lanzhou, and Urumchi, where\udthe dominant land cover is grassland, with an MBE larger than 0.2, and the\udmain aerosol sources are coal combustion and dust. The ORAC algorithm has the\udability to retrieve AOD at different ranges, including high AOD (larger than\ud1.0); however, the stability deceases significantly with increasing AOD,\udespecially when AOD > 1.0. In addition, the ORAC product is\udconsistent with the CARSNET product in winter (December, January, and\udFebruary), whereas other validation results lack matches during winter

Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China