Articles | Volume 26, issue 21
https://doi.org/10.5194/acp-26-15953-2026
https://doi.org/10.5194/acp-26-15953-2026
Technical note
 | 
11 Nov 2026
Technical note |  | 11 Nov 2026

Technical note: Integrating generative AI with physics-based simulations for probabilistic modelling of volcanic clouds

Leonardo Mingari, Arnau Folch, Heribert Pascual, and Manuel Titos

Related authors

Towards the European Tephra Hazard Map: Methodology and Catalogue of Footprints
Arnau Folch, Laura Sandri, Sara Barsotti, Simone Aguiar, Beatriz Martínez Montesinos, Alejandra Guerrero, Pablo Tierz, Heribert Pascual, Eva Hernandez, Leonardo Mingari, and Antonio Costa
EGUsphere, https://doi.org/10.5194/egusphere-2026-4485,https://doi.org/10.5194/egusphere-2026-4485, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Reconstructing tephra fall deposits via ensemble-based data assimilation techniques
Leonardo Mingari, Antonio Costa, Giovanni Macedonio, and Arnau Folch
Geosci. Model Dev., 16, 3459–3478, https://doi.org/10.5194/gmd-16-3459-2023,https://doi.org/10.5194/gmd-16-3459-2023, 2023
Short summary
Data assimilation of volcanic aerosol observations using FALL3D+PDAF
Leonardo Mingari, Arnau Folch, Andrew T. Prata, Federica Pardini, Giovanni Macedonio, and Antonio Costa
Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022,https://doi.org/10.5194/acp-22-1773-2022, 2022
Short summary
Long-term hazard assessment of explosive eruptions at Jan Mayen (Norway) and implications for air traffic in the North Atlantic
Manuel Titos, Beatriz Martínez Montesinos, Sara Barsotti, Laura Sandri, Arnau Folch, Leonardo Mingari, Giovanni Macedonio, and Antonio Costa
Nat. Hazards Earth Syst. Sci., 22, 139–163, https://doi.org/10.5194/nhess-22-139-2022,https://doi.org/10.5194/nhess-22-139-2022, 2022
Short summary

Cited articles

Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Signal Process., 50, 174–188, https://doi.org/10.1109/78.978374, 2002. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a, b
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a, b
Chen, Z., Li, D., Liu, J., Xu, J., and Yang, Q.: Assimilating Observations to Improve Arctic Sea Ice Seasonal Prediction: A Variational Autoencoder Latent Space Particle Filter Approach, J. Geophys. Res.-Oceans, 130, https://doi.org/10.1029/2024JC022206, 2025. a
Download
Short summary
Forecasting volcanic clouds is challenging because it requires many computationally demanding simulations to capture uncertainty. We use artificial intelligence to generate large numbers of realistic forecasts quickly and inexpensively. Tests show that this approach can reproduce the range of possible volcanic cloud behaviours and improve predictions when combined with observations. This could help scientists and decision-makers better understand volcanic hazards.
Share
Altmetrics
Final-revised paper
Preprint