| Novel new approaches to automatically detect and characterize volcanic ash using satellite data are presented. The Spectrally Enhanced Cloud Objects (SECO) ash detection algorithm, combines radiative transfer theory, Bayesian methods, and image processing/computer vision concepts to identify volcanic ash clouds in satellite data with skill that is generally comparable to a human expert, especially with respect to false alarm rate. The SECO method is globally applicable and can be applied to virtually any low earth orbit or geostationary satellite sensor. The new ash detection approach was quantitatively proven to be significantly more skillful than traditional pixel based approaches, including the commonly used "split-window" technique. The performance of the SECO approach is extremely promising and well suited for a variety of new and improved applications. A new approach to retrieve volcanic ash cloud properties from infrared satellite measurements was also developed. The algorithm utilizes an optimal estimation framework to retrieve ash cloud height, mass loading, and effective particle radius. Optimal estimation allows uncertainties in the measurements and forward model to be taken into account and uncertainty estimates for each of the retrieved parameters to be determined. Background atmospheric water vapor, surface temperature, and surface emissivity are explicitly accounted for on a pixel-by-pixel basis, so the algorithm is globally applicable. In addition, the ash cloud retrieval algorithm is unique because it allows the cloud temperature/height to be a free parameter.;Volcanic ash clouds are a major aviation hazard. Fine-grained ash from explosive eruptions can be transported long distances (>1000 km) from the source volcano by atmospheric winds, severely disrupting aviation operations. Volcanic ash clouds are complex and the background environment in which they reside can be as well. Thus, sophisticated satellite remote sensing techniques for extracting ash cloud properties are needed to increase the timeliness and accuracy of volcanic ash advisories and forecasts. As demonstrated using the 2008 eruption of Kasatochi volcano in Alaska, the new theoretical ash remote sensing framework is well suited for advanced applications such as automated volcanic ash cloud alerting and constraining model forecasts of volcanic ash dispersion and removal. |
