Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 12
Articles | Volume 19, issue 12
https://doi.org/10.5194/amt-19-4255-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-4255-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 12
Research article
 | 
30 Jun 2026
Research article |  | 30 Jun 2026

Leveraging machine learning techniques and SEVIRI data to detect volcanic clouds composed of ash, ice, and SO2

Camilo Naranjo, Lorenzo Guerrieri, Stefano Corradini, Matteo Picchiani, Luca Merucci, and Dario Stelitano

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Short summary
This work presents the development of a neural network model for detecting volcanic clouds under challenging conditions, where the cloud contains not only ash but also sulfur dioxide and ice. The presence of ice complicates detection and often leads to failures in traditional methods. Our results show that the neural network improves detection performance and supports the automatic volcanic cloud monitoring, which is crucial for aviation safety.
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