The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation
International audience Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geost...
Published in: | Remote Sensing |
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Main Authors: | , , , , , , |
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Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2021
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Online Access: | https://hal.science/hal-03321305 https://hal.science/hal-03321305/document https://hal.science/hal-03321305/file/remotesensing-13-03128-v3.pdf https://doi.org/10.3390/rs13163128 |
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Open Polar |
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Météo-France: HAL |
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ftmeteofrance |
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English |
topic |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU]Sciences of the Universe [physics] |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU]Sciences of the Universe [physics] Piontek, Dennis Bugliaro, Luca Kar, Jayanta Schumann, Ulrich Marenco, Franco Plu, Matthieu Voigt, Christiane The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU]Sciences of the Universe [physics] |
description |
International audience Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error ... |
author2 |
Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Piontek, Dennis Bugliaro, Luca Kar, Jayanta Schumann, Ulrich Marenco, Franco Plu, Matthieu Voigt, Christiane |
author_facet |
Piontek, Dennis Bugliaro, Luca Kar, Jayanta Schumann, Ulrich Marenco, Franco Plu, Matthieu Voigt, Christiane |
author_sort |
Piontek, Dennis |
title |
The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation |
title_short |
The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation |
title_full |
The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation |
title_fullStr |
The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation |
title_full_unstemmed |
The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation |
title_sort |
new volcanic ash satellite retrieval vacos using msg/seviri and artificial neural networks: 2. validation |
publisher |
HAL CCSD |
publishDate |
2021 |
url |
https://hal.science/hal-03321305 https://hal.science/hal-03321305/document https://hal.science/hal-03321305/file/remotesensing-13-03128-v3.pdf https://doi.org/10.3390/rs13163128 |
genre |
Eyjafjallajökull |
genre_facet |
Eyjafjallajökull |
op_source |
ISSN: 2072-4292 Remote Sensing https://hal.science/hal-03321305 Remote Sensing, 2021, 13 (16), pp.3128. ⟨10.3390/rs13163128⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs13163128 hal-03321305 https://hal.science/hal-03321305 https://hal.science/hal-03321305/document https://hal.science/hal-03321305/file/remotesensing-13-03128-v3.pdf doi:10.3390/rs13163128 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.3390/rs13163128 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
16 |
container_start_page |
3128 |
_version_ |
1810442956204670976 |
spelling |
ftmeteofrance:oai:HAL:hal-03321305v1 2024-09-15T18:05:24+00:00 The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation Piontek, Dennis Bugliaro, Luca Kar, Jayanta Schumann, Ulrich Marenco, Franco Plu, Matthieu Voigt, Christiane Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS) 2021-08 https://hal.science/hal-03321305 https://hal.science/hal-03321305/document https://hal.science/hal-03321305/file/remotesensing-13-03128-v3.pdf https://doi.org/10.3390/rs13163128 en eng HAL CCSD MDPI info:eu-repo/semantics/altIdentifier/doi/10.3390/rs13163128 hal-03321305 https://hal.science/hal-03321305 https://hal.science/hal-03321305/document https://hal.science/hal-03321305/file/remotesensing-13-03128-v3.pdf doi:10.3390/rs13163128 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2072-4292 Remote Sensing https://hal.science/hal-03321305 Remote Sensing, 2021, 13 (16), pp.3128. ⟨10.3390/rs13163128⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2021 ftmeteofrance https://doi.org/10.3390/rs13163128 2024-06-25T00:11:56Z International audience Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error ... Article in Journal/Newspaper Eyjafjallajökull Météo-France: HAL Remote Sensing 13 16 3128 |