Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash
Atmospheric natural hazards pose a risk to people, aircraft and infrastructure. Automated algorithms can detect these hazards from satellite imagery so that the relevant advice can be issued. The transparency and adaptability of these automated algorithms is important to cater to the needs of the en...
Published in: | Quarterly Journal of the Royal Meteorological Society |
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crwiley:10.1002/qj.3230 2024-06-23T07:52:36+00:00 Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash Western, L. M. Rougier, J. Watson, I. M. Natural Environment Research Council 2018 http://dx.doi.org/10.1002/qj.3230 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fqj.3230 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3230 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qj.3230 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3230 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Quarterly Journal of the Royal Meteorological Society volume 144, issue 711, page 581-587 ISSN 0035-9009 1477-870X journal-article 2018 crwiley https://doi.org/10.1002/qj.3230 2024-06-13T04:22:53Z Atmospheric natural hazards pose a risk to people, aircraft and infrastructure. Automated algorithms can detect these hazards from satellite imagery so that the relevant advice can be issued. The transparency and adaptability of these automated algorithms is important to cater to the needs of the end user, who should be able to readily interpret the hazard warning. This means avoiding heuristic techniques. Decision theory is a statistical tool that transparently considers the risk of false positives and negatives when detecting the hazard. By assigning losses to incorrect actions, ownership of the hazard warning is shared between the scientists and risk managers. These losses are readily adaptable depending on the perceived threat of the hazard. This study demonstrates how decision theory can be applied to the detection of atmospheric natural hazards using the example of volcanic ash during an ongoing eruption. The only observations are the difference in brightness temperature between two channels on the SEVIRI sensor. We apply the method to two volcanic eruptions: the 2010 eruption of Eyjafjallajökull, Iceland, and the 2011 eruption of Puyehue‐Cordón Caulle, Chile. The simple probabilistic method appears to work well and is able to distinguish volcanic ash from desert dust, which is a common false positive for volcanic ash. As is made clear, decision theory is a tool for decision support, providing transparency and adaptability, but it still requires careful input from scientists and risk managers. Effectively it provides a space where these groups of experts can meet and convert their shared understanding of a hazard into a choice of action. Article in Journal/Newspaper Eyjafjallajökull Iceland Wiley Online Library Quarterly Journal of the Royal Meteorological Society 144 711 581 587 |
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Wiley Online Library |
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English |
description |
Atmospheric natural hazards pose a risk to people, aircraft and infrastructure. Automated algorithms can detect these hazards from satellite imagery so that the relevant advice can be issued. The transparency and adaptability of these automated algorithms is important to cater to the needs of the end user, who should be able to readily interpret the hazard warning. This means avoiding heuristic techniques. Decision theory is a statistical tool that transparently considers the risk of false positives and negatives when detecting the hazard. By assigning losses to incorrect actions, ownership of the hazard warning is shared between the scientists and risk managers. These losses are readily adaptable depending on the perceived threat of the hazard. This study demonstrates how decision theory can be applied to the detection of atmospheric natural hazards using the example of volcanic ash during an ongoing eruption. The only observations are the difference in brightness temperature between two channels on the SEVIRI sensor. We apply the method to two volcanic eruptions: the 2010 eruption of Eyjafjallajökull, Iceland, and the 2011 eruption of Puyehue‐Cordón Caulle, Chile. The simple probabilistic method appears to work well and is able to distinguish volcanic ash from desert dust, which is a common false positive for volcanic ash. As is made clear, decision theory is a tool for decision support, providing transparency and adaptability, but it still requires careful input from scientists and risk managers. Effectively it provides a space where these groups of experts can meet and convert their shared understanding of a hazard into a choice of action. |
author2 |
Natural Environment Research Council |
format |
Article in Journal/Newspaper |
author |
Western, L. M. Rougier, J. Watson, I. M. |
spellingShingle |
Western, L. M. Rougier, J. Watson, I. M. Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
author_facet |
Western, L. M. Rougier, J. Watson, I. M. |
author_sort |
Western, L. M. |
title |
Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
title_short |
Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
title_full |
Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
title_fullStr |
Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
title_full_unstemmed |
Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
title_sort |
decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1002/qj.3230 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fqj.3230 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3230 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qj.3230 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3230 |
genre |
Eyjafjallajökull Iceland |
genre_facet |
Eyjafjallajökull Iceland |
op_source |
Quarterly Journal of the Royal Meteorological Society volume 144, issue 711, page 581-587 ISSN 0035-9009 1477-870X |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/qj.3230 |
container_title |
Quarterly Journal of the Royal Meteorological Society |
container_volume |
144 |
container_issue |
711 |
container_start_page |
581 |
op_container_end_page |
587 |
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1802643955691552768 |