Articles | Volume 22, issue 3
Nat. Hazards Earth Syst. Sci., 22, 1029–1054, 2022
Nat. Hazards Earth Syst. Sci., 22, 1029–1054, 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 et al.

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Cited articles

Ball, J., Reed, B., Grainger, R., Peters, D., Mather, T., and Pyle, D.: Measurements of the complex refractive index of volcanic ash at 450, 546.7, and 650 nm, J. Geophys. Res., 120, 7747–7757,, 2015. a
Bass, S. F.: Optical Properties of laboratory-generated polar stratospheric particles, PhD thesis, Oxford University, Oxford, (last access: 28 March 2022), 2003. a
Baum, B. A., Soulen, P. F., Strabala, K. I., King, M. D., Ackerman, S. A., Menzel, W. P., and Yang, P.: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase, J. Geophys. Res.-Atmos., 105, 11781–11792,, 2000. a
Brenot, H., Theys, N., Clarisse, L., van Gent, J., Hurtmans, D. R., Vandenbussche, S., Papagiannopoulos, N., Mona, L., Virtanen, T., Uppstu, A., Sofiev, M., Bugliaro, L., Vázquez-Navarro, M., Hedelt, P., Parks, M. M., Barsotti, S., Coltelli, M., Moreland, W., Scollo, S., Salerno, G., Arnold-Arias, D., Hirtl, M., Peltonen, T., Lahtinen, J., Sievers, K., Lipok, F., Rüfenacht, R., Haefele, A., Hervo, M., Wagenaar, S., Som de Cerff, W., de Laat, J., Apituley, A., Stammes, P., Laffineur, Q., Delcloo, A., Lennart, R., Rokitansky, C.-H., Vargas, A., Kerschbaum, M., Resch, C., Zopp, R., Plu, M., Peuch, V.-H., Van Roozendael, M., and Wotawa, G.: EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds, Nat. Hazards Earth Syst. Sci., 21, 3367–3405,, 2021. a, b
Budd, L., Griggs, S., Howarth, D., and Ison, S.: A Fiasco of Volcanic Proportions? Eyjafjallajökull and the Closure of European Airspace, Mobilities, 6, 31–40,, 2011. a
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