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The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

Piontek, Dennis and Bugliaro Goggia, Luca and Schmidl, Marius and Zhou, Daniel K. and Voigt, Christiane (2021) The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development. Remote Sensing. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13163112. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/16/3112


Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications.

Item URL in elib:https://elib.dlr.de/144546/
Document Type:Article
Title:The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bugliaro Goggia, LucaDLR, IPAhttps://orcid.org/0000-0003-4793-0101UNSPECIFIED
Voigt, ChristianeDLR, IPAhttps://orcid.org/0000-0001-8925-7731UNSPECIFIED
Date:6 August 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:Volcanic Ash, Remote Sensing, Artificial Neural Network, Radiative Transfer
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 > Cloud Physics
Deposited By: Piontek, Dennis
Deposited On:14 Oct 2021 08:10
Last Modified:14 Oct 2021 08:10

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