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The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation

Piontek, Dennis and Bugliaro Goggia, Luca and Kar, Jayanta and Schumann, Ulrich and Marenco, Franco and Plu, Matthieu and Voigt, Christiane (2021) The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation. Remote Sensing, pp. 1-36. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13163128. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/16/3128

Abstract

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 of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.

Item URL in elib:https://elib.dlr.de/144549/
Document Type:Article
Title:The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Piontek, DennisDLR, IPAUNSPECIFIED
Bugliaro Goggia, LucaDLR, IPAhttps://orcid.org/0000-0003-4793-0101
Kar, Jayantajayanta.kar (at) nasa.govUNSPECIFIED
Schumann, UlrichDLR, IPAhttps://orcid.org/0000-0001-5255-6869
Marenco, Francofranco.marenco (at) metoffice.gov.ukUNSPECIFIED
Plu, Matthieumatthieu.plu (at) meteo.frUNSPECIFIED
Voigt, ChristianeDLR, IPAhttps://orcid.org/0000-0001-8925-7731
Date:7 August 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs13163128
Page Range:pp. 1-36
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:Volcanic Ash, Remote Sensing, Artificial Neural Network, Eyjafjallajökull, Puyehue-Cordon Caulle, Transport and Dispersion Model
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:other
DLR - Research area:Aeronautics
DLR - Program:L - no assignment
DLR - Research theme (Project):L - no assignment
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics
Institute of Atmospheric Physics > Cloud Physics
Deposited By: Piontek, Dennis
Deposited On:14 Oct 2021 08:14
Last Modified:14 Oct 2021 08:14

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