Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-1029-2022
https://doi.org/10.5194/nhess-22-1029-2022
Research article
 | 
30 Mar 2022
Research article |  | 30 Mar 2022

VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

Luca Bugliaro, Dennis Piontek, Stephan Kox, Marius Schmidl, Bernhard Mayer, Richard Müller, Margarita Vázquez-Navarro, Daniel M. Peters, Roy G. Grainger, Josef Gasteiger, and Jayanta Kar

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Latest update: 23 May 2024
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Short summary
The monitoring of ash dispersion in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. We present an AI-based method that retrieves the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This algorithm, trained on realistic observations simulated with a radiative transfer model, runs operationally at the German Weather Service.
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