Automated Detection of Explosive Volcanic Eruptions Using Satellite-Derived Cloud Vertical Growth Rates

Abstract

Ash rich clouds, produced by explosive volcanic eruptions, are a major hazard to aviation. Unfortunately, explosive volcanic eruptions are not always detected in a timely manner in satellite data. The large optical depth of emergent volcanic clouds greatly limits the effectiveness of multispectral infrared-based techniques for distinguishing between volcanic and nonvolcanic clouds. Shortwave radiation-based techniques require sufficient sunlight and large amounts of volcanic ash, relative to hydrometeors, to be effective. Given these fundamental limitations, a new automated technique for detecting emergent clouds, produced by explosive volcanic eruptions, has been developed. The Cloud Growth Anomaly (CGA) technique utilizes geostationary satellite data to identify cloud objects, near volcanoes, that are growing rapidly in the vertical relative to clouds that formed through meteorological processes. Explosive volcanic events are shown to frequently be a source of rapidly developing clouds that, at a minimum, reach the upper troposphere. As such, the CGA algorithm is effective at determining when a recently formed cloud is possibly the result of an explosive eruptive event. While the CGA technique can be applied to any geostationary satellite sensor, it is most effective when applied to latest generation of meteorological satellites, which provide more frequent images with improved spatial resolution. Using a large collection of geographically diverse explosive eruptions, and several geostationary satellites, the CGA technique is described and demonstrated. A CGA-based eruption alerting tool, which is designed to improve the timeliness of volcanic ash advisories, is also described.