Articles | Volume 22, issue 3
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-270', Anonymous Referee #1, 07 Nov 2021
    • AC1: 'Reply on RC1', Luca Bugliaro, 28 Jan 2022
  • RC2: 'Comment on nhess-2021-270', Anonymous Referee #2, 19 Nov 2021
    • AC2: 'Reply on RC2', Luca Bugliaro, 28 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (14 Feb 2022) by Giovanni Macedonio
AR by Luca Bugliaro on behalf of the Authors (14 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (22 Feb 2022) by Giovanni Macedonio
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.
Final-revised paper