Articles | Volume 15, issue 24
https://doi.org/10.5194/amt-15-7195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-7195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case
Ilaria Petracca
CORRESPONDING AUTHOR
Department of Civil Engineering and Computer Science Engineering,
Tor Vergata University of Rome, 00133 Rome, Italy
Davide De Santis
Department of Civil Engineering and Computer Science Engineering,
Tor Vergata University of Rome, 00133 Rome, Italy
Matteo Picchiani
GEO-K s.r.l., 00133 Rome, Italy
GMATICS s.r.l., 00173 Rome, Italy
Stefano Corradini
Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome,
Italy
Lorenzo Guerrieri
Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome,
Italy
Fred Prata
AIRES Pty. Ltd., Mount Eliza, Victoria 3930, Australia
Luca Merucci
Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome,
Italy
Dario Stelitano
Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome,
Italy
Fabio Del Frate
Department of Civil Engineering and Computer Science Engineering,
Tor Vergata University of Rome, 00133 Rome, Italy
Giorgia Salvucci
Department of Civil Engineering and Computer Science Engineering,
Tor Vergata University of Rome, 00133 Rome, Italy
Giovanni Schiavon
Department of Civil Engineering and Computer Science Engineering,
Tor Vergata University of Rome, 00133 Rome, Italy
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This work presents a new simplified ground-based thermal infrared (TIR) system capable of detecting and retrieving volcanic emissions during both the day and the night. Knowing the location of the instrument and the crater, it is possible to compute the geometry (height and thickness) of a volcanic plume. Furthermore, thanks to a specific filter positioned in front of one of the TIR cameras, it is possible to compute the sulfur dioxide (SO2) content emitted by the volcano at a safe distance from the vent.
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Geostationary satellite data have been used to measure the stratospheric aerosols from the explosive Hunga volcanic eruption by using the data in a novel way. The onboard imager views part of the Earth's limb and data from this region were analysed to generate vertical cross-sections of aerosols high in the atmosphere. The analyses show the hemispheric spread of the aerosols and their vertical structure in layers from 22–28 km in the stratosphere.
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Atmospheric particles include compounds that play a key role in the greenhouse effect and air toxicity. Concurrent observations of these compounds by multiple instruments are presented, following deployment within an urban environment in the Po Valley, one of Europe's pollution hotspots. The study compares these data, highlighting the impact of ground emissions, mainly vehicular traffic and biomass burning, on the absorption of sun radiation and, ultimately, on climate change and air quality.
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Volcanic emissions emit large quantities of gases and primary aerosols that can play an important role in atmospheric chemistry. We present a study of the fate of volcanic bromine emissions from the eruption of Mount Etna around Christmas 2018. Using a numerical model and satellite observations, we analyse the impact of the volcanic plume and how it modifies the composition of the air over the whole Mediterranean basin, in particular on tropospheric ozone through the bromine-explosion cycle.
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The detection and monitoring of volcanic clouds are important for aviation management, climate and weather forecasts. We present in this paper the first comprehensive archive collecting spatial and temporal information about volcanic clouds generated by the 11 largest eruptions of this century. We provide a complete set of state-of-the-art data allowing the development and testing of new algorithms contributing to improve the accuracy of the estimation of fundamental volcanic cloud parameters.
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Short summary
The authors propose a near-real-time procedure for the detection of volcanic clouds by means of Sentinel-3 satellite data and neural networks. The algorithm results in an automatic image classification where ashy pixels are distinguished from other surfaces with remarkable accuracy. The model is considerably faster if compared to other approaches which are time consuming, case specific, and not automatic. The algorithm can be significantly helpful for emergency management during eruption events.
The authors propose a near-real-time procedure for the detection of volcanic clouds by means of...