Suspended volcanic ash poses significant threats to the aviation community. These threats include loss of life and severe damage to aircraft. Current operational volcanic ash detection techniques used at the various Volcanic Ash Advisory Centers (VAAC's) are generally qualitative and require manual analysis. Reliable satellite-based automated ash detection techniques are few and far between due to the difficult nature of separating volcanic clouds from meteorological clouds and other non-volcanic features using reflectance or brightness temperature measurements alone. In order to better isolate the volcanic cloud signal, one must explicitly account for the background conditions, such as surface temperature, surface emissivity, temperature and moisture profiles, and viewing angle. This work has focused on developing robust infrared-based volcanic ash detection techniques that explicitly account for the background signal using ancillary data sources and a fast radiative transfer model. Infrared measurements offer the advantage of day/night independence. It will be shown that these new techniques combined with spatial and temporal information can be used to accurately identify volcanic clouds in an automated, computationally efficient algorithm that is applicable to GOES-R ABI and NPOESS VIIRS data.

In addition, to forecast the dispersion of volcanic ash clouds, an estimate of the cloud height and mass loading is needed. Both of these parameters can be retrieved from the combination of infrared radiances and volcanic ash microphysical models. We will present results of an optimal estimation technique used to retrieve these parameters. Examples of the volcanic ash detection technique, height retrieval, and mass loading retrieval will be presented using SEVIRI and MODIS as proxies for ABI and VIIRS. Simplified versions of the algorithms are applicable to present day operational sensors such as the GOES imager and the AVHRR.