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In this paper, we develop an algorithm based on combining spectral, spatial,and temporal thresholds from the geostationary Spinning Enhanced Visible andInfrared Imager (SEVIRI) daytime measurements to identify and trackdifferent aerosol types, primarily volcanic ash. Contemporary methodstypically do not use temporal information to identify ash. We focus not onlyon the identification and tracking of volcanic ash during theEyjafjallajökull volcanic eruption period beginning in 14 April and ending 17 May2010 but also on a pixel-level classification method for separating various classesin the SEVIRI images. Three case studies on 13, 16, and 17 May areanalyzed in extensive detail with other satellite data including from theModerate Resolution Imaging Spectroradiometer (MODIS), Multi-angle ImagingSpectroradiometer (MISR), and Facility for Airborne Atmospheric Measurements(FAAM) BAe146 aircraft data to verify the aerosol spatial distribution mapsgenerated by the SEVIRI algorithm. Our results indicate that the SEVIRIalgorithm is able to track volcanic ash when the solar zenith angle is lowerthan about 65°. Furthermore, the BAe146 aircraft data show thatthe SEVIRI algorithm detects nearly all ash regions when AOD > 0.2. However, the algorithm has higher uncertainties when AOD is < 0.1 over water and AOD < 0.2 over land. The ash spatialdistributions provided by this algorithm can be used as a critical input andvalidation for atmospheric dispersion models simulated by Volcanic AshAdvisory Centers (VAACs). Identifying volcanic ash is an important firststep before quantitative retrievals of ash concentration can be made

The identification and tracking of volcanic ash using the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI)