QAI_Volc: High-Performance Computing Infrastructure for Volcanic Early Detection and Nowcasting with Quantum AI Technologies

QAI_Volc is an infrastructural and methodological initiative within the ROSE (Reinforcement of the Observational Systems of the Earth) infrastructural project of INGV, funded by the Italian Ministry of University and Research. The initiative aims to substantially strengthen the operational capabilities of the Etna Volcano Observatory for the analysis and near-real-time forecasting of complex volcanic phenomena, by integrating: (i) a reinforced High-Performance Computing (HPC) environment, (ii) advanced AI-based early detection systems, and (iii) emerging Quantum Artificial Intelligence approaches for volcanic nowcasting.

Launched in 2026, QAI_Volc is conceived as a 24-month program combining infrastructure upgrades, operational tool consolidation, and frontier research and training activities, with direct relevance for monitoring activities at Etna and Stromboli.

Key objectives

QAI_Volc pursues one overarching goal: deploying an advanced computational infrastructure that couples HPC and Quantum AI to improve early detection and nowcasting of volcanic activity, with a clear operational orientation.

The objectives are structured into three Work Packages:

HPC infrastructure reinforcement (WP1)
Consolidate and evolve the Etna Observatory computing center into a resilient, scalable hub supporting real-time data ingestion, physics-based modeling (digital-twin-oriented workflows), deep-learning pipelines, and hybrid classical–quantum experimentation.

ETNAS enhancement and extension (WP2)
Consolidate and upgrade the ETNAS early-detection system for rapid identification of lava fountains and magmatic intrusions at Etna, and assess transferability to Stromboli, through multi-source data integration and robust AI models.

Quantum AI methodologies for volcanic nowcasting (WP3)
Explore and test quantum and hybrid QC–HPC models for short-term forecasting of dynamic volcanic phenomena (lava flows, ash clouds, eruptive evolution), with rigorous benchmarking against classical deep-learning approaches.

Scientific rationale

Volcanic phenomena such as lava fountains, magmatic intrusions, lava-flow emplacement and propagation, ash-cloud dispersion, and ground deformation are typical examples of nonlinear, multi-scale, and uncertainty-dominated systems, where time-critical decision support requires advanced computational and data-driven capabilities.

Quantum AI combines quantum-computing paradigms with AI/ML algorithms and offers a forward-looking route to handle high-dimensional representations, complex optimization, and hybrid physics-data integration problems. QAI_Volc leverages consolidated expertise at the Etna Observatory in AI pipelines and operational multi-source monitoring, while building readiness for quantum-enabled workflows through a controlled, progressive research plan.

Technologies

QAI_Volc integrates enabling technologies across three coordinated layers.

High-Performance Computing and infrastructure readiness

Expansion of heterogeneous compute resources (CPU/GPU) and high-performance storage
Low-latency internal networking and scalable workflows
Integration with Quantum Cloud services via software gateways and hybrid execution pipelines
Containerized environments and orchestration tools for reproducibility and portability
Dedicated technical support (HPC/Research Software Engineering profile) to ensure sustainability and operational continuity

AI for early detection (ETNAS)

Deep-learning models for rapid detection of paroxysmal activity and magmatic intrusions
Multi-source data fusion (geostationary satellite data, visual/thermal networks, multiparametric monitoring)
Robustness improvements (false positives reduction, onset sensitivity, handling missing data)
Generalization testing and adaptation to different eruptive regimes (Stromboli)

Quantum AI for nowcasting

Variational Quantum Circuits (VQC), Quantum CNN, quantum kernels
Quantum autoregressive approaches for short-term prediction
Hybrid QC–HPC workflows with systematic benchmarking versus classical deep learning
Application-oriented proof-of-concepts on real monitoring scenarios at Etna and Stromboli

Quantum Artificial Intelligence School

QAI_Volc contributes to the Quantum Artificial Intelligence School, an advanced training initiative organized under the scientific leadership of the DEMETRA research line of the ROSE project, whose dedicated web page is available on the TechnoLab website.

The School is designed as a transversal enabling action supporting multiple ROSE research lines and aims to strengthen internal competences in high-performance computing, artificial intelligence, and emerging quantum technologies applied to Earth and environmental sciences.

The Quantum AI School is developed in close collaboration with the University of Catania. Within this framework, QAI_Volc provides a full scientific and infrastructural contribution, particularly in relation to volcanic monitoring, early detection, and nowcasting applications.

Training activities combine lectures with hands-on laboratories based on real INGV datasets and operational workflows, with access to HPC infrastructures and Quantum Cloud resources. The School supports the development of skills in:

HPC and hybrid HPC–Quantum Cloud infrastructures
AI-based monitoring and early detection systems (including ETNAS)
Quantum AI methodologies and QC–HPC integration for volcanic nowcasting pipelines

The School contributes to the long-term sustainability of ROSE by fostering interdisciplinary expertise and training researchers, technologists, and students at the interface between geophysics, artificial intelligence, and quantum computing.

Key information

The Quantum AI School will take place at the end of September 2026.
Participant registration will open in early March 2026.

Project organization

QAI_Volc Principal Investigators (INGV - Etna Volcano Observatory, Catania):

Flavio Cannavò (ETNAS team)
Claudia Corradino (TechnoLab)
Salvatore Mangiagli (Operations Room Unit)

ROSE Project Coordinator

Ciro Del Negro (INGV - Etna Volcano Observatory, Catania)

Contributions:

QAI_Volc involves coordinated contributions from the Etna Volcano Observatory, including:

the Operations Room Unit (validation/testing in operational conditions),
the TechnoLab (HPC/AI/Quantum AI development for nowcasting applications),
the ETNAS development team (evolution and integration of the operational early-detection system).

Workpackages (WP)

WP1 – HPC infrastructure reinforcement and Quantum Cloud integration (Lead: Salvo Mangiagli)
Infrastructure upgrade, hybrid readiness, workflow scalability, and dedicated specialist technical support.

WP2 – ETNAS: development, consolidation, and extension (Lead: Flavio Cannavò)
Enhanced AI algorithms, multi-source fusion, improved operational robustness, and transferability testing to Stromboli.

WP3 – Quantum AI for volcanic nowcasting (Lead: Claudia Corradino)
Development of quantum and hybrid models, QC–HPC integration, benchmarking against classical approaches, and experimental applications to real volcanic scenarios.

Contact

For scientific and organizational information on QAI_Volc:
Email: flavio.cannavoThis email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

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DEMETRA: Investigating the Interplay Between Volcanic Activity and Climate Change

dataset demetra

DEMETRA is a research line of the ROSE (Reinforcement of the Observational Systems of the Earth) infrastructural project of INGV, funded by the Italian Ministry of University and Research. Launched in 2025 as a three-year initiative, DEMETRA investigates how volcanic activity influences the Earth’s climate system, with a particular focus on regional impacts in the Euro-Mediterranean area.

By integrating numerical modelling, advanced data analytics and multidisciplinary observations, DEMETRA addresses both explosive volcanic eruptions and long-term passive degassing processes. The project aims to improve the understanding of volcanic forcing on climate variability and change, and to enhance predictive capabilities relevant for climate risk assessment.

Key objectives

improving the predictability and early warning of climatic impacts associated with volcanic activity;
identifying and fingerprinting volcanic signals in Euro-Mediterranean climate variability;
quantifying volcanic forcing from both eruptive events and quiescent gas emissions.

DEMETRA contributes to strengthening INGV’s observational and modelling capabilities within ROSE, leveraging advanced computational infrastructures and innovative methodologies developed at the TechnoLab of the Etna Volcano Observatory.

technologies

Scientific rationale

Explosive volcanic eruptions represent one of the most important natural drivers of climate variability. Large eruptions inject sulfur-bearing gases into the stratosphere, enhancing the aerosol layer and reducing incoming solar radiation, with consequent surface cooling. Beyond this direct radiative forcing, volcanic activity can trigger complex dynamical responses affecting atmospheric circulation, precipitation patterns, ocean–atmosphere coupling and longer-term climate variability.

In addition to explosive events, DEMETRA investigates the climatic role of persistent passive degassing from quiescent volcanoes and geothermal regions. These processes release greenhouse gases into the atmosphere and contribute to a background radiative forcing that remains poorly constrained in current climate models. Particular attention is devoted to periods of clustered eruptions, which may delay climate recovery and lead to prolonged environmental and societal impacts.

By addressing both eruptive and quiescent volcanic processes, DEMETRA aims to reduce uncertainties in the representation of volcanic forcing in regional and global climate systems.

Objectives

DEMETRA pursues three closely connected scientific objectives aimed at improving the understanding and predictability of volcanic impacts on climate.

Predictability and early warning

Develop an integrated framework combining observations, numerical modelling and data-driven approaches to assess the predictability of volcanic impacts on climate and to support future climate risk mitigation strategies.

Fingerprinting volcanic impacts

Identify and attribute the signatures of major volcanic eruptions in European and Euro-Mediterranean climate variability, clarifying the physical mechanisms linking volcanic forcing to regional climate responses.

Quantifying volcanic forcing

Quantify the sources, fluxes and atmospheric dispersion of volcanic and tectonic greenhouse gas emissions in order to estimate their net long-term climatic forcing.

Technologies

DEMETRA integrates advanced computational and observational technologies to bridge volcanology and climate science and to investigate volcanic–climate interactions across multiple spatial and temporal scales.

Artificial Intelligence and Machine Learning techniques are used to analyse large satellite data archives, enabling rapid detection and characterization of volcanic plumes, aerosols and gas emissions, as well as the nowcasting of their atmospheric dispersion.

Deep learning methods support the development of high-resolution climate model emulators, allowing large ensembles of simulations to be generated with substantially reduced computational costs. These emulators complement global and regional climate models and enable probabilistic assessments of post-eruption climate responses.

High-performance computing infrastructures underpin data-intensive analyses and large ensemble simulations, while emerging Quantum Computing methodologies are explored as a long-term perspective to enhance the scalability and efficiency of complex Earth system modelling.

Observational activities combine satellite measurements with ground-based geochemical and geophysical data. Advanced instrumentation is used to quantify volcanic and geothermal gas fluxes, determine their chemical and isotopic composition, and constrain subsurface fluid pathways, providing key inputs for atmospheric dispersion models and climate impact assessments.

DEMETRA will answer these questions:

Are the climatic impacts of volcanic eruptions predictable?
What are the impacts of strong volcanic eruptions on Euro-Mediterranean climate and their driving mechanisms?
What are the changes in atmospheric composition due to volcanic greenhouse gas emissions?

Quantum Artificial Intelligence School

As a strategic extension of the activities promoted within ROSE, DEMETRA supports the Quantum Artificial Intelligence School, an advanced training initiative designed to strengthen competences in artificial intelligence, high-performance computing and emerging quantum methodologies for geoscience and climate applications.

The school adopts a strongly application-oriented approach, combining lectures with hands-on laboratories based on real INGV data, access to HPC infrastructures and cloud-based quantum resources. Within DEMETRA, the Quantum AI School contributes to the long-term sustainability of advanced computational infrastructures and to the development of a stable, interdisciplinary research community at the interface between volcanology, climate science and computational geosciences.

Key information

The Quantum AI School will take place at the end of September 2026.
Participant registration will open in early March 2026.

Project organization

DEMETRA Principal Investigators

Vito Zago (INGV – Etna Volcano Observatory, Catania)

Davide Zanchettin (Università Ca’ Foscari Venezia)

ROSE Project Coordinator

Ciro Del Negro (INGV – Etna Volcano Observatory, Catania)

Research Units (RU)

DEMETRA involves an international and multidisciplinary team organized into six Research Units (RU), covering complementary expertise in climate science, volcanology, geochemistry, geophysics, artificial intelligence and historical climate reconstruction.

RU1 – Davide Zanchettin (Università Ca’ Foscari Venezia)
RU2 – Vito Zago (INGV – Etna Volcano Observatory, Catania)
RU3 – Alessandra Sciarra (INGV – Sezione Roma 1)
RU4 – Paola Del Carlo (INGV – Sezione di Pisa)
RU5 – Franco Tassi (Università di Firenze)
RU6 – Luigi Dallai (Sapienza Università di Roma)

Workpackages (WP)

Research activities are structured into eight interconnected Work Packages (WP) covering coordination, technological development, modelling, observations, gas emission studies and dissemination.

Contact

For scientific and organizational information on the DEMETRA research line:
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

header earth telescope

DEMETRA: Investigating the Interplay Between Volcanic Activity and Climate Change

dataset demetra The DEMETRA project is part of the Earth Telescope research programme of INGV and is aimed at advancing our understanding of how volcanic activity shapes the Earth’s climate system. Launched in 2024 with an initial term through 2027, DEMETRA lays the groundwork for a lasting scientific effort that leverages key enabling technologies and multidisciplinary observations to deepen knowledge of our planet and strengthen our ability to anticipate future change.

DEMETRA brings together climate modelers, specialists in computational dynamics, complex systems and artificial intelligence, volcanologists, paleoclimatologists and historians. Their combined expertise makes it possible to identify the fingerprints of major volcanic eruptions on regional climates, assess the predictability of their local impacts, and quantify the climatic forcing associated with greenhouse gas emissions from volcanic and geothermal regions — including during quiescent phases.

Technologies

technologies

Scientific rationale

Strong explosive volcanic eruptions release significant amounts of gases, such as SO₂, that can reach the stratosphere. These enhance the stratospheric aerosol layer, reducing the amount of solar radiation reaching Earth’s surface (negative radiative forcing) and leading to surface cooling. But this direct radiative response is only the most immediate effect.

Beyond this thermodynamical impact, volcanic eruptions trigger wider dynamical responses throughout the Earth system. Precipitation patterns may shift, altering river runoff and even influencing components of ocean circulation, with further consequences for regional and global climate. The complexity and interconnected nature of these processes make it challenging to fully comprehend the chain of interactions involved.

An additional, still insufficiently quantified factor is persistent passive degassing. Even in the absence of explosive eruptions, quiescent volcanoes and geothermal systems continuously release greenhouse gases, contributing to a long-term positive radiative forcing. This contribution to the overall greenhouse gas budget remains poorly constrained, yet it may represent a non-negligible component of the climate system.

To address these questions, DEMETRA is built upon three core objectives that together define its scientific and methodological roadmap:

Project organization

The project involves a broad international team of researchers, organized into six Research Units and led by six Principal Investigators, both from within INGV and from external institutions: