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The Communication and Dissemination Plan (D9.2) introduces the DT-GEO dissemination and communication strategy and its implementation plan. This document will be a reference framework for planning the activities and evaluating the impact of communication and dissemination activities throughout the project.

This document describes and explains the reasons behind the design choices of DT-GEO official website, which has been created to offer information and bring the project closer to both collaborators and the general public. Thus, this is a report that supports the demonstration deliverable D9.1.

This document describes and explains the reasons behind the first video project for DT-GEO and how it will be marketed and promoted.

This deliverable is the result of the preliminary work of WP2 partners on the collection of the requirements from the DTCs WPs; this work has been performed in tasks T2.1, T2.2, T2.3 with inputs received from T5.1, T6.1, T7.1 and T8.1. The analysis of the requirements has led to the design of the first prototype of the DT-GEO architecture, whose base components are also described in this report.

This document lays out the Project Management and Quality Guidelines for DT-GEO project. The guidelines aim to describe the processes that will ensure that the desired quality of all tasks and deliverables are achieved. It describes the project´s structure and governing bodies, the tools to promote a successful development and the mechanisms to avoid potential risks.

The Millennium Eruption of Paektu volcano, on the border of China and North Korea, generated tephra deposits that extend >1000 km from the vent, making it one of the largest eruptions in historical times. Based on observed thicknesses and compositions of the deposits, the widespread tephra dispersal is attributed to two eruption phases fuelled by chemically distinct magmas that produced both pyroclastic flows and fallout deposits. We used an ensemble-based method with a dual step inversion, in combination with the FALL3D atmospheric tephra transport model, to constrain these two different phases. The volume of the two distinct phases has been calculated. The results indicate that about 3-16 km3 (with a best estimate of 7.2 km3) and 4-20 km3 (with a best estimate of 9.3 km3) of magma were erupted during the comendite and trachyte phases of the eruption, respectively. Eruption rates of up to 4 × 108 kg/s generated plumes that extended 30-40 km up into the stratosphere during each phase.

identify volcanic unrest and originting force distributions causing it

Short and long-term probabilistic volcano hazard assessments entail the fusion of data from multiple sources with the realisation and subsequent combination of hundreds/thousands of scenarios spanning the range of system uncertainties (model inputs and parameterisations, boundary conditions, etc). Computational workflows are middleware software layers that manage and orchestrate in an automated way the multiple steps and tasks involved in this process, from data acquisition and preparation, to model executions and post-process in centralised (HPC) and/or cloud computing infrastructures. This contribution presents some examples from on-going European projects tackling computational geohazards on HPC/cloud infrastructures. For the short-term hazard assessment, the DT-GEO project (2022-2025, Grant Agreement No 101058129) is implementing a number of workflows conducting precise data-informed early warning systems and hazard assessments by harnessing world-class computational (FENIX, EuroHPC) and data (EPOS) research infrastructures. The volcano-related workflows in DT-GEO include: (i) merging of multi-parametric data from ground-based and remote observation systems (on-site monitoring networks and satellites) with global modelling of magma and rock dynamics and with AI approach; (ii) merging of real-time geostationary satellite observations with the FALL3D model to generate deterministic and ensemble-based probabilistic forecast products; (iii) merging of real-time multi-parametric data from ground-based and remote observation systems with deterministic modelling of lava flow propagation and inundation areas and; (iv) air-quality data and AI in a volcanic gas dispersal forecast context to improve operational Early Warning Systems. On the other hand, the EuroHPC ChEESE Center of Excellence (CoE) is conducting an ensemble-based volcanic dispersal across multiple scales that will lead to the first European tephra hazard map at scale covering, simultaneously, long-range dis

The geophysical research community has developed a relatively large amount of numerical codes and scientific methodologies which are able to numerically simulate through physics the extreme behavior of the Earth systems (for example: volcanoes, tsunamis earthquakes, etc). Furthermore, nowadays, large volumes of data have been acquired and, even near real-time data streams are accessible. Therefore, Earth scientist currently have on their hands the possibility of monitoring these events through sophisticated approaches using the current leading edge computational capabilities provided by pre-exascale computing infrastructures. The implementation and deployments of 12 Digital Twin Components (DTCs), addressing different aspects of geophysical extreme events is being carried out by DT-GEO, a project funded under the Horizon Europe programme (2022-2025). Each DTC is intended as self-contained entity embedding flagship simulation codes, Artificial Intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows which will are executed independently or coupled DTCs in a centralized HPC and/or virtual cloud computing research infrastructure.

Probabilistic volcanic hazard assessment is a standard methodology based on running a deterministic hazard quantification tool multiple times to explore the full range of uncertainty in the input parameters and boundary conditions, in order to probabilistically quantify the variability of outputs accounting for such uncertainties. Nowadays, different volcanic hazards are quantified by means of this approach. Among these, volcanic gas emission is particularly relevant given the threat posed to human health if concentrations and exposure times exceed certain thresholds. There are different types of gas emissions but two main scenarios can be recognized: hot buoyant gas emissions from fumaroles and the ground and dense gas emissions feeding density currents that can occur, e.g., in limnic eruptions. Simulation tools are available to model the evolution of critical gas concentrations over an area of interest. Moreover, in order to perform probabilistic hazard assessments of volcanic gases, simulations should account for the natural variability associated to aspects such as seasonal and daily wind conditions, localized or diffuse source locations, and gas fluxes. Here we present VIGIL (automatized probabilistic VolcanIc Gas dIspersion modeLling), a new Python tool designed for managing the entire simulation workflow involved in single and probabilistic applications of gas dispersion modelling. VIGIL is able to manage the whole process from meteorological data processing, needed to run gas dispersion in both the dilute and dense gas flow scenarios, to the post processing of models' outputs. Two application examples are presented to show some of the modelling capabilities offered by VIGIL.

Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on buildings, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height and physical properties of particles or meteorological fields represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based data assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA, and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the local ensemble transform Kalman filter (LETKF) is used to assimilate satel

Volcanic eruptions are among the most jeopardizing natural events due to their potential impacts on life, assets, and the environment. In particular, atmospheric dispersal of volcanic tephra and aerosols during explosive eruptions poses a serious threat to life and has significant consequences for infrastructures and global aviation safety. The volcanic island of Jan Mayen, located in the North Atlantic under trans-continental air traffic routes, is considered the northernmost active volcanic area in the world with at least five eruptive periods recorded during the last 200 years. However, quantitative hazard assessments on the possible consequences for the air traffic of a future ash-forming eruption at Jan Mayen are nonexistent. This study presents the first comprehensive long-term volcanic hazard assessment for the volcanic island of Jan Mayen in terms of ash dispersal and concentration at different flight levels. In order to delve into the characterization and modeling of that potential impact, a probabilistic approach based on merging a large number of numerical simulations is adopted, varying the volcano's eruption source parameters (ESPs) and meteorological scenario. Each ESP value is randomly sampled following a continuous probability density function (PDF) based on the Jan Mayen geological record. Over 20 years of meteorological data is considered in order to explore the natural variability associated with weather conditions and is used to run thousands of simulations of the ash dispersal model FALL3D on a 2 km resolution grid. The simulated scenarios are combined to produce probability maps of airborne ash concentration, arrival time, and persistence of unfavorable conditions at flight levels 50 and 250 (FL050 and FL250). The resulting maps can serve as an aid during the development of civil protection strategies, to decision-makers and aviation stakeholders, in assessing and preventing the potential impact of a future ash-rich eruption at Jan Mayen.

Operational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not account for model input uncertainties and their propagation in time during transport. An ensemble-based forecast strategy has been implemented in the FALL3D-8.1 atmospheric dispersal model to configure, execute, and post-process an arbitrary number of ensemble members in a parallel workflow. In addition to intra-member model domain decomposition, a set of inter-member communicators defines a higher level of code parallelism to enable future incorporation of model data assimilation cycles. Two types of standard products are automatically generated by the ensemble post-process task. On one hand, deterministic forecast products result from some combination of the ensemble members (e.g., ensemble mean, ensemble median, etc.) with an associated quantification of forecast uncertainty given by the ensemble spread. On the other hand, probabilistic products can also be built based on the percentage of members that verify a certain threshold condition. The novel aspect of FALL3D-8.1 is the automatisation of the ensemble-based workflow, including an eventual model validation. To this purpose, novel categorical forecast diagnostic metrics, originally defined in deterministic forecast contexts, are generalised here to probabilistic forecasts in order to have a unique set of skill scores valid to both deterministic and probabilistic forecast contexts. Ensemble-based deterministic and probabilistic approaches are compared using different types of observation datasets (satellite cloud detection and retrieval and deposit thickness observations) for the July 2018 Ambae eruption in the Vanuatu archipelago and the April 2015 Calbuco eruption in Chile. Both ensemble-based approaches outperform sin

The 11th Galileo Conference in Barcelona (May 23-26, 2023) addressed Exascale computing challenges in geosciences. With 78 participants from 15 countries, it focused on European-based research but welcomed contributions from worldwide institutions. The conference had four sessions covering HPC applications, data workflows, computational geosciences, and EuroHPC infrastructures. It featured keynote presentations, poster sessions, and breakout sessions, including Master Classes for 22 Early Career Scientists supported by EGU. This document represents the consensus among participants, capturing outcomes from breakout sessions and acknowledging diverse opinions and approaches., The 11th Galileo Conference of the European Geosciences Union (EGU) focused on "Solid Earth and Geohazards in the Exascale Era." This abstract presents the main outcomes and conclusions from the conference breakout sessions, which aimed to provide recommendations for the future of solid earth research. The discussions highlighted the challenges and opportunities associated with high-performance computing (HPC) in solid earth sciences. The key findings include the need for collaboration between computer scientists and solid earth domain-specific scientists, the importance of portability software layers for different hardware architectures, the adoption of programming models for easier development and deployment of applications, the necessity of HPC training at all career stages, the improvement of accessibility and authentication mechanisms for European machines, and the readiness of urgent computing services for natural catastrophes. The conference also emphasized the significance of sustainable funding, software engineering best practices, and the development of modular and interoperable codes and workflows. Overall, the conference provided insights into the current status of computational solid earth research and offered recommendations for future advancements in the field., European Geosciences Union (EGU), the EuroHPC Center of Excellence for Exascale in Solid Earth (ChEESE) under Grant Agreement No 101093038 (https://cheese2.eu), and the European Union's Next Generation/PRTR Program through grant PCI2022-134973-2., Peer Reviewed, "Article signat per 115 autors/es:" Folch, A., Bhihe, C., Caviedes-Vouillième, D., de la Puente, J., Esposti Ongaro, T., Garg, D, Gibbons, S. J., Kaus, B., Monterrubio, M., Räss, L., Reis, C., Scaini, C., Srivastava, N., Vilarrasa, V., Zwinger, T.", Postprint (author's final draft)

The 11th Galileo Conference of the European Geosciences Union (EGU) focused on "Solid Earth and Geohazards in the Exascale Era." This abstract presents the main outcomes and conclusions from the conference breakout sessions, which aimed to provide recommendations for the future of solid earth research. The discussions highlighted the challenges and opportunities associated with high-performance computing (HPC) in solid earth sciences. The key findings include the need for collaboration between computer scientists and solid earth domain-specific scientists, the importance of portability software layers for different hardware architectures, the adoption of programming models for easier development and deployment of applications, the necessity of HPC training at all career stages, the improvement of accessibility and authentication mechanisms for European machines, and the readiness of urgent computing services for natural catastrophes. The conference also emphasized the significance of sustainable funding, software engineering best practices, and the development of modular and interoperable codes and workflows. Overall, the conference provided insights into the current status of computational solid earth research and offered recommendations for future advancements in the field.

Interdisciplinary digital twins are becoming able to mimic the different Earth system domains with unrivalled precision, providing analyses, forecasts, uncertainty quantification, and ¿what if¿ scenarios for natural and anthropogenic hazards from their genesis to propagation and impacts. The EU DT-GEO project (2022-2025) is deploying a prototype digital twin on geophysical extremes consisting of interrelated Digital Twin Components (DTCs), intended as self-contained containerised entities embedding simulation codes, Artificial Intelligence (AI) layers, large volumes of nearly-real-time data streams, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralised High Performance Computing (HPC) and virtual cloud computing Research Infrastructures (RIs). These DTCs, actually a first step towards a digital twin on Geophysical Extremes integrated in the Destination Earth (DestinE) initiative, will deal with geohazards from earthquakes, volcanoes, and tsunamis by harnessing world-class computational (EuroHPC) and data (EPOS) Research Infrastructures, operational monitoring networks, and leading-edge research and academia partnerships. In particular, 4 DTCs of the 12 in DT-GEO will address different volcanic hazards. DTC-V1 will merge multi-parametric data from ground- based and remote observation systems (on-site monitoring networks and satellites) with global modelling of magma and rock dynamics and with AI approach. DTC-V2 will merge real-time geostationary satellite observations with the FALL3D model using the on-line data assimilation PDAF system to generate deterministic and ensemble-based probabilistic forecast products. DTC-V3 will merge real-time multi- parametric data from ground-based and remote observation systems with deterministic modelling of lava flow propagation and inundation areas including Bayesian modelling of vent opening. Finally, DTC-V4 will consider air-quality data and AI in a gas

The project Digital Twin for GEOphysical extremes-(DT-GEO) aims to use Digital Twin Components to create replicas of physical systems, serving as a virtual laboratory to study natural extreme events. The ratio- nale is the intrinsic risks of potentially catastrophic events to anthropic activities, infrastructures, and cultural heritage. In the framework of the project, this paper describes, how the DTC workflow architecture is designed, focusing on flexibility, scalability, and maintainability, and how it is further developed. To demonstrate how ICT efforts can expand horizons in Geosciences, an application to volcanic hazard is presented taking as a case study the 2019 volcanic eruption of Raikoke (Kuril Islands).

Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth) we created an optimized HPC-based workflow coined PVHA_HPC-WF to develop Probabilistic Volcanic Hazard Assessment (PVHA) for a specific volcano. Increasing the computational capabilities of current PVHA products, PVHA_HPC-WF provides probability and hazard maps, with uncertainty, for tephra fallout at ground and airborne ash concentration and time-persistence at strategic flight levels, exploring the natural variability in Eruptive Source Parameters (ESPs) and wind conditions through a large number of ash dispersal simulations with the model Fall3D. Among other tests, we showcased the workflow through a live exercise for Campi Flegrei, proving the feasibility and usefulness for end-users, such as the Centre of Competence of the Italian Civil Protection PLINIVS and ARISTOTLE, of such hazard evaluations to produce useful short-term impact assessment of tephra ground load at the scale of a country, in particular over mobility networks (road, railways, seaports and airports) and electrical networks, in an operational environment, dealing with real- time performance-distributed workflow. We ran 300 large-scale and high-resolution tephra dispersal simulations with the Flagship code Fall3D on MareNostrum at BSC fetching weather forecast from GFS, and we processed them on the computer cluster ADA at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) of Bologna fetching real-time monitoring data from Osservatorio Vesuviano surveillance system. Results show the ability of the PVHA_HPC-WF to perform PVHA in a reasonable time, with a sufficient level of detail, and therefore its usefulness for civil protection officials and society in reliably assessing volcanic hazard.

Which is the EuroHPC deployment roadmap, policy and HPC infrastructure access modes? Which are the research funding opportunities for geosciences from Horizon Europe (REA) or other EC Directorates-General (DGs)? How can European geoscientists better partner to benefit from the Exascale transition opportunities?

In recent years, there has been a growing interest in ensemble-based based approaches for modeling volcanic plumes. The development of advanced ensemble modeling techniques enables the exploration of novel methods for the incorporation of real observations into tephra dispersal models using ensemble- based data assimilation techniques. However, traditional data assimilation algorithms, including ensemble Kalman filter methods, can yield suboptimal state estimates for positive-definite variables such as volcanic aerosols and tephra deposits. We present two new ensemble-based data assimilation techniques for semi- positive-definite variables with highly skewed uncertainty distributions, such as deposit mass loading. The proposed methods are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption and the 946 CE eruption of Baekdu volcano, the so-called Millennium eruption, one of the largest eruptions in historic times based on widespread tephra dispersal. The FALL3D dispersal model was used to perform an ensemble of runs in order to simulate the transport and deposition of tephra for different model configurations. Subsequently, deposit thickness measurements are assimilated to reconstruct the tephra deposit and improve the first-guess results, obtained from a simple ensemble forecast. An assessment of the assimilation methods is carried out using an independent dataset of observations in terms of different evaluation metrics. The methodologies presented here represent promising alternatives for the assimilation of real observations in operational models.

The DT-GEO project (2022-2025), funded under the Horizon Europe topic call INFRA-2021-TECH-01-01, is implementing an interdisciplinary digital twin for modelling and simulating geophysical extremes at the service of research infrastructures and related communities. The digital twin consists of interrelated Digital Twin Components (DTCs) dealing with geohazards from earthquakes to volcanoes to tsunamis and that harness world-class computational (FENIX, EuroHPC) and data (EPOS) Research Infrastructures, operational monitoring networks, and leading-edge research and academic partnerships in various fields of geophysics. The project is merging and assembling latest developments from other European projects and EuroHPC Centers of Excellence to deploy 12 DTCs, intended as self-contained containerised entities embedding flagship simulation codes, artificial intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralised HPC and virtual cloud computing Research Infrastructures (RIs). Each DTC addresses specific scientific questions and circumvents technical challenges related to hazard assessment, early warning, forecasts, urgent computing, or geo-resource prospection. This presentation summarises the results form the first year of the project including the digital twin architecture and the (meta)data structures enabling (semi-)automatic discovery, contextualisation, and orchestration of software (services) and data assets. This is a preliminary step before verifying the DTCs at 13 Site Demonstrators and starts a long-term community effort towards a twin on Geophysical Extremes integrated in the Destination Earth (DestinE) initiative.

Rapid advances in Digital Twin (DT) technology have enabled real-time virtual replicas of physical processes. This paradigm shift, however, presents challenges in integrating field data into seismology DTs. This contribution explores the complexities encountered when transitioning from raw seismic field data to the development of accurate DT models, focusing on forward and inverse waveform simulation workflows. Integrated flows are pivotal for a smooth transition from field data to DT. Some, such as adjoint waveform tomography and shaking simulations, pose particular difficulties stemming from uncertainties in the location and characterization of seismic sources. Additionally, very high frequency signals, while imperative to improve resolution, drastically increase computational costs, and low signal-to-noise ratios further complicate accurate extraction of valuable information. Uneven receiver distributions further exacerbate these challenges. Moreover, earthquake rupture models can only be obtained from seismotectonic information, including geometry and kinematic data. Unfortunately, this data is often imprecise and derived from multiple sources, making it vital to account for uncertainties during integration and in the results. Furthermore, seismic experiments generates vast amounts of information, which must be processed, selected and effectively utilized to construct reliable DTs. The management and analysis of such extensive datasets need robust data handling strategies and efficient storage solutions. High-Performance Computing (HPC) plays a critical role in mitigating the computational burden associated with DTs. This contribution encourages discussion on the complexities involved in transitioning field data to DTs, emphasizing the importance of addressing large data volumes, leveraging HPC capabilities and efficiently accounting for uncertainties. An integrated approach that unifies data acquisition, preprocessing, model development and simulation enables s

This event provides each participant with the opportunity to interact with AE colleagues in a unique manner generating manifold links for the future. As stated by Don Dingwell, Vice-President of Academia Europaea and Director of the AE Munich Hub, ¿given the current environment of Europe and its neighbours, such interactions have never been more important

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018¿2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023¿2026).

In recent years, there has been a growing interest in ensemble approaches for modelling the atmospheric transport of volcanic aerosol, ash, and lapilli (tephra). The development of such techniques enables the exploration of novel methods for incorporating real observations into tephra dispersal models. However, traditional data assimilation algorithms, including ensemble Kalman filter (EnKF) methods, can yield suboptimal state estimates for positive-definite variables such as those related to volcanic aerosols and tephra deposits. This study proposes two new ensemble-based data assimilation techniques for semi-positive-definite variables with highly skewed uncertainty distributions, including aerosol concentrations and tephra deposit mass loading: the Gaussian with non-negative constraints (GNC) and gamma inverse-gamma (GIG) methods. The proposed methods are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conducted considering two independent datasets of deposit thickness measurements: an assimilation dataset and a validation dataset. Different evaluation metrics (e.g. RMSE, MBE, and SMAPE) are computed for the validation dataset, and the results are compared to two references: the ensemble prior mean and the EnKF analysis. Results show that the assimilation leads to a significant improvement over the first-guess results obtained from the simple ensemble forecast. The evidence from this study suggests that the GNC method was the most skilful approach and represents a promising alternative for assimilation of volcanic fallout data. The spatial distributions of the tephra fallout deposit thickness and volume according to the GNC analysis are in good agreement with estimations based on field measurements and isopach maps reported in previous studies. On the other hand, although it is an interesting approach, the GIG m

The atmospheric dispersion of gases (of natural or industrial origins) can be very hazardous to life and the environment if the concentration of some gas species overcome specie-specific thresholds. In this context, the natural variability associated to the natural phenomena has to be explored to provide robust probabilistic gas dispersion hazard assessments. VIGIL-1.3 (automatic probabilistic VolcanIc Gas dIspersion modeLling) is a Python simulation tool born to automatize the complex and time-consuming simulation workflow required to process a large number of gas dispersion numerical simulations. It is interfaced with two models: a dilute (DISGAS) and a dense gas (TWODEE-2) dispersion model. The former is used when the density of the gas plume at the source is lower than the atmospheric density (e.g. fumaroles), the latter when the gas density is higher than the atmosphere and the gas accumulates on the ground and may flow due to the density contrast with the atmosphere to form a gravity current (e.g. cold CO2 flows). In the enhancement of the code towards a higher-scale computing, here we present the ongoing improvements aimed to extend some code functionalities such as memory management, modularity revision, and full-ensemble uncertainty on gas dispersal scenarios (e.g. sampling techniques for gas fluxes and source locations). Optimizations are also provided in terms of tracking errors, redesignation of the input file, validation of data provided by the users, and addition of the Latin hypercube sampling (LHS) for the post-processing of model outputs. All these new features will be issued in the future release of the code (VIGIL-2.0) in order to facilitate the users which could run VIGIL on laptops or large supercomputer, and to widen the spectrum of model applications from routinely operational forecast of volcanic gas to long-term hazard and/or risk assessments purposes.

The second phase (2023-2026) of the EuroHPC Center of Excellence for Exascale in Solid Earth (ChEESE-2P), funded by HORIZON-EUROHPC-JU-2021-COE-01 under the Grant Agreement No 101093038, will prepare 11 European flagship codes from different geoscience domains. Codes will be optimised in terms of performance on different types of accelerators, scalability, containerisation, and continuous deployment and portability across tier-0/tier-1 European systems as well as on novel hardware architectures emerging from the EuroHPC Pilots (EuPEX/OpenSequana and EuPilot/RISC-V) by co-designing with mini-apps. Flagship codes and workflows will be combined to farm a new generation of 9 Pilot Demonstrators (PDs) and 15 related Simulation Cases (SCs) representing capability and capacity computational challenges selected based on their scientific importance, social relevance, or urgency. On the other hand, the first phase of ChEESE was pivotal in leveraging an ecosystem of European projects and initiatives tackling computational geohazards that will benefit from current and upcoming exascale EuroHPC infrastructures. In particular, Geo-INQUIRE (2022-2024, GA No 101058518) and DT-GEO (2022-2025, GA No 101058129) are two on-going Horizon Europe projects relevant to the Solid Earth ecosystem. The former will provide virtual and trans-national service access to data and state-of-the-art numerical models and workflows for monitoring and simulation of the dynamic processes in the geosphere at unprecedented levels of detail and precision. The later will deploy a prototype Digital Twin (DT) on geophysical extremes including 12 self-contained Digital Twin Components (DTCs) addressing specific hazardous phenomena from volcanoes, tsunamis, earthquakes, and anthropogenically-induced extremes to conduct precise data-informed early warning systems, forecasts, and hazard assessments across multiple time scales. All these initiatives liaise, align, and synergise with EPOS and longer-term mission-like

DT-GEO is a project proposed to deal with natural or anthropogenically induced geohazards (earthquakes, volcanoes, landslides and tsunamis) by deploying a Digital Twin of the planet. The prototype will provide a way to visualize, manipulate and understand the response to hypothetical or on-going events by integrating data acquisition and models. Due to the complexity of the development, the project has been divided into different work packages and components. The volcanic phenomena package includes 4 Digital Twin Components (DTCs): volcanic unrest, volcanic ash clouds and ground accumulations, lava flows, and volcanic gas dispersal. The volcanic ash and dispersal deposition component implements a workflow for atmospheric dispersal and ground deposition forecast systems. The workflow is composed of four general units. The first one is the Numerical Weather Prediction (NWP) acquisition (provided by external institutions) refers to both: automatic obtention of the forecast (up to few days ahead) or the reanalysis (preprocess data from the past) in global or regional scales at different resolutions. Then, the Triggering and Eruption Source Parameters (ESP) is based on predefined communications channels and prioritized by an accuracy rank. The FALL3D model setup and run ensemble simulations, resulting from perturbing ESP values within a range. Finally, the postprocess refers to the compilation of the simulations into hazard maps.