Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays
During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars have proved to be a fundamental instrument to derive eruptive source par...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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ftunivgeneve:oai:unige.ch:aou:unige:87531 2023-10-01T03:55:50+02:00 Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays Marzano, Frank S. Picciotti, Errico Di Fabio, Saverio Montopoli, Mario Mereu, Luigi Degruyter, Wim Bonadonna, Costanza Ripepe, Maurizio 2016 https://archive-ouverte.unige.ch/unige:87531 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1109/TGRS.2016.2578282 https://archive-ouverte.unige.ch/unige:87531 unige:87531 info:eu-repo/semantics/openAccess ISSN: 0196-2892 IEEE transactions on geoscience and remote sensing, no. 99 (2016) info:eu-repo/classification/ddc/550 info:eu-repo/semantics/article Text Article scientifique info:eu-repo/semantics/publishedVersion 2016 ftunivgeneve https://doi.org/10.1109/TGRS.2016.2578282 2023-09-07T07:33:29Z During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars have proved to be a fundamental instrument to derive eruptive source parameters. We extend this capability to include an early-warning detection scheme within the overall volcanic ash radar retrieval methodology. This scheme, called the volcanic ash detection (VAD) algorithm, is based on a hybrid technique using both fuzzy logic and conditional probability. Examples of VAD applications are shown for some case studies, including the Icelandic Grímsvötn eruption in 2011, the Eyjafjallajökull eruption in 2010, and the Italian Mt. Etna volcano eruption in 2013. Estimates of the eruption onset from the radar-based VAD module are compared with infrasonic array data. One-dimensional numerical simulations and analytical model estimates of MFR are also discussed and intercompared with sensor-based retrievals. Results confirm in all cases the potential of MW weather radar for ash plume monitoring in near real time and its complementarity with infrasonic array for early-warning system design. Article in Journal/Newspaper Eyjafjallajökull Université de Genève: Archive ouverte UNIGE IEEE Transactions on Geoscience and Remote Sensing 54 11 6292 6306 |
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Université de Genève: Archive ouverte UNIGE |
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English |
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info:eu-repo/classification/ddc/550 |
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info:eu-repo/classification/ddc/550 Marzano, Frank S. Picciotti, Errico Di Fabio, Saverio Montopoli, Mario Mereu, Luigi Degruyter, Wim Bonadonna, Costanza Ripepe, Maurizio Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays |
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info:eu-repo/classification/ddc/550 |
description |
During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars have proved to be a fundamental instrument to derive eruptive source parameters. We extend this capability to include an early-warning detection scheme within the overall volcanic ash radar retrieval methodology. This scheme, called the volcanic ash detection (VAD) algorithm, is based on a hybrid technique using both fuzzy logic and conditional probability. Examples of VAD applications are shown for some case studies, including the Icelandic Grímsvötn eruption in 2011, the Eyjafjallajökull eruption in 2010, and the Italian Mt. Etna volcano eruption in 2013. Estimates of the eruption onset from the radar-based VAD module are compared with infrasonic array data. One-dimensional numerical simulations and analytical model estimates of MFR are also discussed and intercompared with sensor-based retrievals. Results confirm in all cases the potential of MW weather radar for ash plume monitoring in near real time and its complementarity with infrasonic array for early-warning system design. |
format |
Article in Journal/Newspaper |
author |
Marzano, Frank S. Picciotti, Errico Di Fabio, Saverio Montopoli, Mario Mereu, Luigi Degruyter, Wim Bonadonna, Costanza Ripepe, Maurizio |
author_facet |
Marzano, Frank S. Picciotti, Errico Di Fabio, Saverio Montopoli, Mario Mereu, Luigi Degruyter, Wim Bonadonna, Costanza Ripepe, Maurizio |
author_sort |
Marzano, Frank S. |
title |
Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays |
title_short |
Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays |
title_full |
Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays |
title_fullStr |
Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays |
title_full_unstemmed |
Near-Real-Time Detection of Tephra Eruption Onset and Mass Flow Rate Using Microwave Weather Radar and Infrasonic Arrays |
title_sort |
near-real-time detection of tephra eruption onset and mass flow rate using microwave weather radar and infrasonic arrays |
publishDate |
2016 |
url |
https://archive-ouverte.unige.ch/unige:87531 |
genre |
Eyjafjallajökull |
genre_facet |
Eyjafjallajökull |
op_source |
ISSN: 0196-2892 IEEE transactions on geoscience and remote sensing, no. 99 (2016) |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1109/TGRS.2016.2578282 https://archive-ouverte.unige.ch/unige:87531 unige:87531 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1109/TGRS.2016.2578282 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
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54 |
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11 |
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6292 |
op_container_end_page |
6306 |
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1778524621485637632 |