Identifying aerosols above clouds from the Spinning Enhanced Visible and Infrared Imager (SEVIRI)

To be submitted to IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2 Abstract-Geostationary data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) in conjunction with A-Train data are used to develop an algorithm for detecting absorbing aerosols above liquid clouds (AAC). The detection relies on spectral signatures, textural characteristics, and time-dependent spectral variation of SEVIRI data. A-Train data including the Ozone Monitoring Instrument (OMI) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used as reference for the SEVIRI algorithm development. The 15-min repeat cycle of SEVIRI provides the capability for identifying aerosols above various cloud types with an OMI aerosol index (AI) value exceeding 0.5 and a cloud optical thickness (COT) of at least 5 at 0.64 µm. The user accuracy of this algorithm is ~80.5% when using both spectral and textural tests. When adding " temporal consistency " tests into the algorithm, the user accuracy increases to ~90.8%. This algorithm can be used to detect and study the daytime variation of AAC from a satellite remote sensing standpoint.