Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-1029-2022
© 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/nhess-22-1029-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Mar 2022
Research article |
| 30 Mar 2022
| 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
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Dennis Piontek
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Stephan Kox
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
now at: Telespazio Germany GmbH, Darmstadt, Germany
Marius Schmidl
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
now at: MTU Aero Engines AG, Munich, Germany
Bernhard Mayer
Ludwig-Maximilians Universität, Meteorologisches Institut,
Munich, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Richard Müller
Deutscher Wetterdienst, Offenbach, Germany
Margarita Vázquez-Navarro
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
now at: European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
Daniel M. Peters
Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
now at: RAL Space, STFC Rutherford Appleton Laboratory, Harwell, UK
Roy G. Grainger
COMET, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
Josef Gasteiger
Ludwig-Maximilians Universität, Meteorologisches Institut,
Munich, Germany
now at: Faculty of Physics, University of Vienna, Vienna, Austria
Jayanta Kar
Science System and Applications, Inc., Hampton, VA, USA
Science Directorate, NASA Langley Research Center, Hampton, VA, USA
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Cited
11 citations as recorded by crossref.
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties J. Mayer et al. 10.5194/amt-17-5161-2024
- Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review R. Mota et al. 10.3390/rs16101789
- Retrieval of Volcanic Sulfate Aerosols Optical Parameters from AHI Radiometer Data A. Filei et al. 10.1007/s00376-024-3105-2
- Neural networks-based ‘first-response’ detection of volcano-tectonic seismic events in poorly monitored active volcanoes: the case of Pico de Orizaba (Citlaltépetl), Mexico U. Que-Salinas et al. 10.1016/j.jsames.2025.105756
- Observations of microphysical properties and radiative effects of a contrail cirrus outbreak over the North Atlantic Z. Wang et al. 10.5194/acp-23-1941-2023
- EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds H. Brenot et al. 10.5194/nhess-21-3367-2021
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo R. Müller & U. Pfeifroth 10.5194/amt-15-1537-2022
- An ensemble of state-of-the-art ash dispersion models: towards probabilistic forecasts to increase the resilience of air traffic against volcanic eruptions M. Plu et al. 10.5194/nhess-21-2973-2021
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
6 citations as recorded by crossref.
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties J. Mayer et al. 10.5194/amt-17-5161-2024
- Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review R. Mota et al. 10.3390/rs16101789
- Retrieval of Volcanic Sulfate Aerosols Optical Parameters from AHI Radiometer Data A. Filei et al. 10.1007/s00376-024-3105-2
- Neural networks-based ‘first-response’ detection of volcano-tectonic seismic events in poorly monitored active volcanoes: the case of Pico de Orizaba (Citlaltépetl), Mexico U. Que-Salinas et al. 10.1016/j.jsames.2025.105756
- Observations of microphysical properties and radiative effects of a contrail cirrus outbreak over the North Atlantic Z. Wang et al. 10.5194/acp-23-1941-2023
5 citations as recorded by crossref.
- EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds H. Brenot et al. 10.5194/nhess-21-3367-2021
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo R. Müller & U. Pfeifroth 10.5194/amt-15-1537-2022
- An ensemble of state-of-the-art ash dispersion models: towards probabilistic forecasts to increase the resilience of air traffic against volcanic eruptions M. Plu et al. 10.5194/nhess-21-2973-2021
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
Latest update: 03 Nov 2025
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.
The monitoring of ash dispersion in the atmosphere is an important task for satellite remote...
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