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Atmospheric aerosol in the Amazon: results from the ORAC retrieval algorithm using AATSR

Andrew Sayer(1), Gareth Thomas(1) and Don Grainger(1)

(1) University of Oxford, Clarendon Laboratory, Oxford OX1 3PU, United Kingdom

Abstract

The central problem in aerosol retrievals from satellite radiometers is partitioning the observed top-of-atmosphere (TOA) reflectance into that arising from the surface and the portion from atmospheric aerosol and gas scattering and absorption. Sensors with multiple viewing geometries, such as the dual-viewing Advanced Along-Track Scanning Radiometer (AATSR), are well-suited to this task as they are able to sample more than one part of the atmospheric phase function and surface bidirectional reflectance distribution function (BRDF) in each observation. The Oxford-RAL Aerosol and Clouds (ORAC) retrieval scheme has been used to process a 5-year time series of aerosol data over the Amazon. ORAC is an optimal estimation retrieval scheme, which allows the use of a priori data on atmospheric and surface properties where it is available, and provides important quality control diagnostics and uncertainty estimates on the retrieved data products. Seasonal cycles of aerosol and surface properties are presented, showing variations in patterns of biomass burning and dust transport, as well as the generally high interannual consistency of seasonal behaviour.