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Sensors 2018, 18(2), 369; https://doi.org/10.3390/s18020369

Comparing Two Independent Satellite-Based Algorithms for Detecting and Tracking Ash Clouds by Using SEVIRI Sensor

1
Institute of Methodologies for Environmental Analysis (IMAA), Italian Research Council (CNR), 85050 Tito Scalo, Potenza, Italy
2
Met Office, FitzRoy Road, Exeter, Devon EX1-3PB, UK
3
School of Engineering, University of Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy
*
Author to whom correspondence should be addressed.
Received: 10 January 2018 / Revised: 23 January 2018 / Accepted: 25 January 2018 / Published: 27 January 2018
(This article belongs to the Section Remote Sensors)
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Abstract

The Eyjafjallajökull (Iceland) volcanic eruption of April–May 2010 caused unprecedented air-traffic disruption in Northern Europe, revealing some important weaknesses of current operational ash-monitoring and forecasting systems and encouraging the improvement of methods and procedures for supporting the activities of Volcanic Ash Advisory Centers (VAACs) better. In this work, we compare two established satellite-based algorithms for ash detection, namely RSTASH and the operational London VAAC method, both exploiting sensor data of the spinning enhanced visible and infrared imager (SEVIRI). We analyze similarities and differences in the identification of ash clouds during the different phases of the Eyjafjallajökull eruption. The work reveals, in some cases, a certain complementary behavior of the two techniques, whose combination might improve the identification of ash-affected areas in specific conditions. This is indicated by the quantitative comparison of the merged SEVIRI ash product, achieved integrating outputs of the RSTASH and London VAAC methods, with independent atmospheric infrared sounder (AIRS) DDA (dust-detection algorithm) observations. View Full-Text
Keywords: Eyjafjallajökull; ash clouds; SEVIRI; AIRS; RSTASH; London VAAC method Eyjafjallajökull; ash clouds; SEVIRI; AIRS; RSTASH; London VAAC method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Falconieri, A.; Cooke, M.C.; Filizzola, C.; Marchese, F.; Pergola, N.; Tramutoli, V. Comparing Two Independent Satellite-Based Algorithms for Detecting and Tracking Ash Clouds by Using SEVIRI Sensor. Sensors 2018, 18, 369.

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