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New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate

Characterizing the size and settling velocity of pyroclastic fragments injected into the atmosphere during volcanic eruptions (i.e., tephra) is crucial to the forecasting of plume and cloud dispersal. Optical disdrometers have been integrated into volcano monitoring networks worldwide in order to be...

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Autores principales: Freret-Lorgeril, V., Bonadonna, C., Rossi, E., Poulidis, A. P., Iguchi, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931165/
https://www.ncbi.nlm.nih.gov/pubmed/35301402
http://dx.doi.org/10.1038/s41598-022-08711-1
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author Freret-Lorgeril, V.
Bonadonna, C.
Rossi, E.
Poulidis, A. P.
Iguchi, M.
author_facet Freret-Lorgeril, V.
Bonadonna, C.
Rossi, E.
Poulidis, A. P.
Iguchi, M.
author_sort Freret-Lorgeril, V.
collection PubMed
description Characterizing the size and settling velocity of pyroclastic fragments injected into the atmosphere during volcanic eruptions (i.e., tephra) is crucial to the forecasting of plume and cloud dispersal. Optical disdrometers have been integrated into volcano monitoring networks worldwide in order to best constrain these parameters in real time. Nonetheless, their accuracy during tephra fallout still needs to be assessed. A significant complication is the occurrence of particle aggregates that modify size and velocity distributions of falling tephra. We made the first use of the Thies Clima Laser Precipitation Monitor (LPM) for tephra-fallout detection at Sakurajima volcano (Japan), which is characterized by a lower size detection window with respect to more commonly used disdrometers (e.g., Parsivel(2)) and can more easily distinguish different falling objects. For the first time, individual particles have been distinguished from most aggregates based on disdrometer data, with the potential to provide useful grain-size information in real time. In case of negligible aggregation, LPM and collected sample-based estimates are in agreement for both grain-size and sedimentation rate. In case of significant aggregation, particle shape analyses and a dedicated drag equation are used to filter out aggregates from LPM data that also provide good agreement with collected tephra samples.
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spelling pubmed-89311652022-03-21 New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate Freret-Lorgeril, V. Bonadonna, C. Rossi, E. Poulidis, A. P. Iguchi, M. Sci Rep Article Characterizing the size and settling velocity of pyroclastic fragments injected into the atmosphere during volcanic eruptions (i.e., tephra) is crucial to the forecasting of plume and cloud dispersal. Optical disdrometers have been integrated into volcano monitoring networks worldwide in order to best constrain these parameters in real time. Nonetheless, their accuracy during tephra fallout still needs to be assessed. A significant complication is the occurrence of particle aggregates that modify size and velocity distributions of falling tephra. We made the first use of the Thies Clima Laser Precipitation Monitor (LPM) for tephra-fallout detection at Sakurajima volcano (Japan), which is characterized by a lower size detection window with respect to more commonly used disdrometers (e.g., Parsivel(2)) and can more easily distinguish different falling objects. For the first time, individual particles have been distinguished from most aggregates based on disdrometer data, with the potential to provide useful grain-size information in real time. In case of negligible aggregation, LPM and collected sample-based estimates are in agreement for both grain-size and sedimentation rate. In case of significant aggregation, particle shape analyses and a dedicated drag equation are used to filter out aggregates from LPM data that also provide good agreement with collected tephra samples. Nature Publishing Group UK 2022-03-17 /pmc/articles/PMC8931165/ /pubmed/35301402 http://dx.doi.org/10.1038/s41598-022-08711-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Freret-Lorgeril, V.
Bonadonna, C.
Rossi, E.
Poulidis, A. P.
Iguchi, M.
New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
title New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
title_full New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
title_fullStr New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
title_full_unstemmed New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
title_short New insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
title_sort new insights into real-time detection of tephra grainsize, settling velocity and sedimentation rate
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931165/
https://www.ncbi.nlm.nih.gov/pubmed/35301402
http://dx.doi.org/10.1038/s41598-022-08711-1
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