Automated Volcanic Cloud Identification, Tracking, and Characterization Using JPSS and GOES-R

Volcanic clouds, which are an aviation hazard, are complex and the background
environment in which they reside can be as well. Thus, sophisticated, automated, remote
sensing algorithms are needed to transform large volumes of satellite data into actionable
information for increasing the timeliness and value of volcanic ash advisories and
forecasts. The need for automated algorithms has never been greater, as data volumes are
significantly increasing with the advent of the next generation of operational
meteorological satellites. In addition, no single satellite sensor provides optimal spatial,
temporal, and spectral capabilities for accurately detecting and characterizing all types of
volcanic clouds. In an effort to fully utilize next generation geostationary and low earth
orbit satellite measurements for real-time volcanic cloud applications, National Oceanic
and Atmospheric Administration (NOAA), in collaboration with the University of
Wisconsin, has developed the Volcanic Cloud Analysis Toolkit (VOLCAT). VOLCAT
utilizes many different satellite sensors to generate alerts when volcanic unrest or an
eruption is detected. VOLCAT also automatically tracks and characterizes volcanic
clouds and provides information needed to constrain and validate dispersion model
forecasts. Through advanced use of spectral, spatial, and temporal information, the
VOLCAT algorithms are capable of automatically detecting a broad range of volcanic
clouds, including opaque multi-component (ash, ice, and SO2 ) clouds. Several Volcanic
Ash Advisory Centers and volcano observatories utilize VOLCAT products. An
overview of the unique VOLCAT algorithm suite, along with examples that illustrate the
benefits of next generation satellites and a multi-sensor approach, will be presented.