A correlation method for volcanic ash detection using hyperspectral infrared measurements

Abstract

Remote satellite detection of airborne volcanic ash is important for mitigating hazards to aviation and for calculating plume altitudes. Infrared sounders are essential for detecting ash, as they can distinguishing aerosol type and can be used day and night. While broadband sensors are mainly used for this purpose, they have inherent limitations. Typically, water and ice can mask volcanic ash, while wind blown dust can yield false detection. High spectral resolution sounders should be able to overcome some of these limitations. However, existing detection methods are not easily applicable to hyperspectral sounders and there is therefore a pressing need for novel techniques. In response, we propose a sensitive and robust volcanic ash detection method for hyperspectral sounders based on correlation coefficients and demonstrate it on IASI observations. We show that the method differentiates ash from clouds and dust. Easy to implement, it could contribute to operational volcanic hazard mitigation.