Articles | Volume 26, issue 21
https://doi.org/10.5194/acp-26-15953-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-15953-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Integrating generative AI with physics-based simulations for probabilistic modelling of volcanic clouds
Leonardo Mingari
CORRESPONDING AUTHOR
Geosciences Barcelona (GEO3BCN-CSIC), Barcelona, Spain
Arnau Folch
Geosciences Barcelona (GEO3BCN-CSIC), Barcelona, Spain
Heribert Pascual
Geosciences Barcelona (GEO3BCN-CSIC), Barcelona, Spain
Manuel Titos
Department of Signal Theory, Telematics and Communications, Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
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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).
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Volcanic ash threatens communities near European volcanoes and aircraft across the continent. A new study has produced the first European hazard map for volcanic dispersal in the atmosphere, using more than 53,000 simulations of ash clouds from twelve European volcanoes, run on one of Europe's most powerful supercomputers. The results show where ash is likely to fall and which flight regions could be dangerous. Simulations are stored in an open catalogue that lets end-users update the hazard.
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
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Two novel techniques for ensemble-based data assimilation, suitable for semi-positive-definite variables with highly skewed uncertainty distributions such as tephra deposit mass loading, are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption in Chile. The deposit spatial distribution and the ashfall volume according to the analyses are in good agreement with estimations based on field measurements and isopach maps reported in previous studies.
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
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We present a new implementation of an ensemble-based data assimilation method to improve forecasting of volcanic aerosols. This system can be efficiently integrated into operational workflows by exploiting high-performance computing resources. We found a dramatic improvement of forecast quality when satellite retrievals are continuously assimilated. Management of volcanic risk and reduction of aviation impacts can strongly benefit from this research.
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
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This work addresses a quantitative hazard assessment on the possible impact on air traffic of a future ash-forming eruption on the island of Jan Mayen. Through high-performance computing resources, we numerically simulate the transport of ash clouds and ash concentration at different flight levels over an area covering Iceland and the UK using the FALL3D model. This approach allows us to derive a set of probability maps explaining the extent and persisting concentration conditions of ash clouds.
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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.
Forecasting volcanic clouds is challenging because it requires many computationally demanding...
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