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| Titel |
Automatic volcanic ash detection from MODIS observations using a back-propagation neural network |
| VerfasserIn |
T. M. Gray, R. Bennartz |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 12 ; Nr. 8, no. 12 (2015-12-08), S.5089-5097 |
| Datensatznummer |
250116715
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| Publikation (Nr.) |
 copernicus.org/amt-8-5089-2015.pdf |
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| Zusammenfassung |
| Due to the climate effects and aviation threats of volcanic
eruptions, it is important to accurately locate ash in the
atmosphere. This study aims to explore the accuracy and reliability
of training a neural network to identify cases of ash using
observations from the Moderate Resolution Imaging Spectroradiometer
(MODIS). Satellite images were obtained for the following eruptions:
Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008;
Grímsvötn, northeastern Iceland, 2011; Chaitén,
southern Chile, 2008; Puyehue-Cordón Caulle, central Chile,
2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The
Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model
was used to obtain ash concentrations for the same
archived eruptions. Two back-propagation neural networks were then
trained using brightness temperature differences as inputs obtained
via the following band combinations: 12–11,
11–8.6, 11–7.3, and 11 μm. Using
the ash concentrations determined via HYSPLIT, flags were created to
differentiate between ash (1) and no ash (0) and SO2-rich
ash (1) and no SO2-rich ash (0) and used as output. When
neural network output was compared to the test data set, 93 % of
pixels containing ash were correctly identified and 7 % were
missed. Nearly 100 % of pixels containing SO2-rich ash
were correctly identified. The optimal thresholds, determined using
Heidke skill scores, for ash retrieval and SO2-rich ash
retrieval were 0.48 and 0.47, respectively. The networks show
significantly less accuracy in the presence of high water vapor,
liquid water, ice, or dust concentrations. Significant errors are
also observed at the edge of the MODIS swath. |
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