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Spectrally Enhanced Cloud Objects—A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements: 2. Cloud object analysis and global application
Author(s) -
Pavolonis Michael J.,
Sieglaff Justin,
Cintineo John
Publication year - 2015
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2014jd022969
Subject(s) - volcanic ash , remote sensing , geostationary orbit , satellite , cloud computing , computer science , multispectral image , cloud top , meteorology , environmental science , radiative transfer , volcano , geology , geography , engineering , physics , quantum mechanics , seismology , aerospace engineering , operating system
Abstract A new approach for quantitatively detecting volcanic ash and dust from satellite has been developed. The Spectrally Enhanced Cloud Objects (SECO) algorithm utilizes a combination of radiative transfer theory, a statistical model, and image processing techniques to identify volcanic ash and dust clouds in satellite imagery with a very low false alarm rate. This fully automated technique is globally applicable (day and night) and can be adapted to a wide range of low Earth orbit and geostationary satellite sensors or even combinations of satellite sensors. The SECO algorithm consists of four primary components: conversion of satellite measurements into robust spectral metrics, application of a Bayesian method to estimate the probability that a given satellite pixel contains volcanic ash and/or dust, construction of cloud objects, and the selection of cloud objects deemed to have the physical attributes consistent with volcanic ash and/or dust clouds. The first two components of the SECO algorithm were described in Part 1 of this study. The final two components are described in this paper. In addition, case studies and a global analysis are utilized to illustrate the benefits of the SECO approach relative to the traditional “split window” ash/dust detection technique. The SECO algorithm can form the basis for more advanced applications such as volcanic cloud alerting and data assimilation.