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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.

xplosive volcanic eruptions can inject high quantities of magmatic materials into the atmosphere representing a risk to life and society. To quantify the potential impacts of a future eruption or to forecast what will happen in the next few hours when a volcano is erupting, atmospheric dispersion models are commonly used providing important information for civil protection and other stakeholders. Spatiotemporal distributions of volcanic ash in volcanic plumes are used as an input by a numerical simulation of tephra dispersal and are called eruption source parameters (ESPs). They have been poorly constrained and therefore their variation between models affects volcanic tephra hazard assessment. Since the goodness of ESPs increases with knowledge of the dynamics of eruptive columns, reconstruction of volcanic columns from past eruptions will improve the assessment of volcanic hazards for future eruptions. In this work we take advantage of recent advances in computational capabilities and modeling in order to robustly reconstruct the dynamics of eruption columns from past eruptions. We do that by applying a synergistic approach between atmospheric dispersion models capable of reproducing the transport of volcanic ash due to atmospheric wind, eruption cloud dynamics models that resolve the ascending and the horizontal spreading of umbrella cloud, and inversion methods able to estimate ESPs using geological data information of tephra deposits. Specifically, we use the latest version of the ash dispersal model FALL3D that allows us to determine EPSs by inverting field data using the novel GNC (Gaussian with non-negative constants) ensemble-based inversion method, and the eruption cloud dynamics model SK-3D that accurately resolves the turbulence of the volcanic plumes. As an application, we focus on Campi Flegrei (CF) caldera, in Italy. CF is currently a densely populated area under busy air traffic routes where the monitoring system of the Vesuvius Observatory highlights

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

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.

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

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.

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.

A Digital Twin Component (DTC) provides users with digital replicas of different components of the Earth system through unified frameworks integrating real-time observations and state-of-the-art numerical models. Scenarios of extreme events for natural hazards can be studied from the genesis to propagation and impacts using a single DTC or multiple coupled DTCs. The EU DT-GEO project (2022-2025) is implementing a prototype digital twin on geophysical extremes consisting of 12 interrelated Digital Twin Components, intended as self-contained and containerised software entities embedding numerical model codes, management of real-time data streams and data assimilation methodologies. DTCs can be deployed and executed in centralized High Performance Computing (HPC) and cloud computing Research Infrastructures (RIs). In particular, the DTC-V2 is implementing an ensemble-based automated operational system for deterministic and probabilistic forecast of long-range ash dispersal and local-scale tephra fallout. The system continuously screens different ground-based and satellite-based data sources and a workflow is automatically triggered by a volcanic eruption to stream and pre-process data, its ingestion into the FALL3D dispersal model, a centralized or distributed HPC model execution, and the post-processing step. The DTCs will provide capability for analyses, forecasts, uncertainty quantification, and "what if" scenarios for natural and anthropogenic hazards, with a long-term ambition towards the Destination Earth mission-like initiative.

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., The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program under the ChEESE project, grant agreement no. 823844. We thank the multi-year PRACE Project Access “Volcanic ash hazard and forecast” (ID 2019215114)., Peer Reviewed, Postprint (published version)

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, We acknowledge the Partnership for Advanced Computing in Europe (PRACE) for awarding us access to the Joliot-Curie supercomputer at the CEA’s Very Large Computing Center (TGCC, France). Andrew T. Prata acknowledges funding from the European Commission, H2020 Marie Skłodowska-Curie Actions (STARS (grant no. 754433)). We thank the anonymous reviewers for their insightful comments and suggestions., Peer Reviewed, Postprint (published version)

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

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 out, This work has been partially funded by the H2020 Center of Excellence for Exascale in Solid Earth (ChEESE) under the Grant Agreement 823 844 and the multi-year PRACE Project Access “Volcanic Ash Hazard and Forecast” (ID 2019 215 114)., Peer Reviewed, Postprint (published version)

This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November–December 2018). This system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF). It combines multiple data sources both global and locally available like high-resolution surface networks, AMDAR data from local aircraft flights, soundings, AIRS retrievals, high-resolution GOES-16 wind estimates, and local radar data. Hourly analyses with grid spacing of 10 km are generated along with warm-start 36-h ensemble-forecasts, which are initialized from the rapid refresh analyses every three hours. A preliminary evaluation shows that a forecast error reduction is achieved due to the assimilated observations. However, cold-start forecasts initialized from the Global Forecasting System Analysis slightly outperform the ones initialized from the regional assimilation system discussed in this paper. The system uses a multi-physics approach, focused on the use of different cumulus and planetary boundary layer schemes allowing us to conduct an evaluation of different model configurations over central Argentina. We found that the best combinations for forecasting surface variables differ from the best ones for forecasting precipitation, and that differences among the schemes tend to dominate the forecast ensemble spread for variables like precipitation. Lessons learned from this experimental system are part of the legacy of the RELAMPAGO field campaign for the development of advanced operational data assimilation systems in South America., We are very thankful to the Argentinian National Meteorological Service (ANMS), the National Scientific and Technical Research Council (CONICET), the University of Buenos Aires (UBA) and the Atmospheric and Sea Research Center (CIMA), who support this project. We acknowledge the Cheyenne HPC resources (doi:https://doi.org/10.5065/D6RX99HX) from NCAR's Computational and Information Systems Laboratory, National Science Foundation (project code UIUC0012). Also, PIDDEF 16/2014, PICT 2014–1000, PICT 2017–0221 and PICT 2018–3202 grants partially funded this project. SWN acknowledges support from National Science Foundation grant AGS-1661799. For the LETKF-WRF a modified version of the code available at https://github.com/takemasa-miyoshi/letkf was used, including the radar observations operator. For post processing issues NCL, Python and R packages were used. Finally, we acknowledge Dr. Victoria Galligani for her suggestions and the anonymous reviewers for their enrichment comments., Peer Reviewed, "Article signat per 17 autors/es: María Eugenia Dillon, Paula Maldonado, Paola Corrales, Yanina García Skabar, Juan Ruiz, Maximiliano Sacco, Federico Cutraro, Leonardo Mingari, Cynthia Matsudo, Luciano Vidal, Martin Rugna, María Paula Hobouchian, Paola Salio, Stephen Nesbitt, Celeste Saulo, Eugenia Kalnay, Takemasa Miyoshi", Postprint (author's final draft)

This paper presents model validation results for the latest version release of the FALL3D atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. The model validation is based on the new FALL3D-8.0 test suite, which comprises a set of four real case studies that encapsulate the major features of the model; namely, the simulation of long-range fine volcanic ash dispersal, volcanic SO2 dispersal, tephra fallout deposits and the dispersal and deposition of radionuclides. The first two test suite cases (i.e. the June 2011 Puyehue-Cordón Caulle ash cloud and the June 2019 Raikoke SO2 cloud) are validated against geostationary satellite retrievals and demonstrate the new FALL3D data insertion scheme. The metrics used to validate the volcanic ash and SO2 simulations are the structure, amplitude and location (SAL) metric and the figure of merit in space (FMS). The other two test suite cases (i.e. the February 2013 Mt. Etna ash cloud and associated tephra fallout deposit, and the dispersal of radionuclides resulting from the 1986 Chernobyl nuclear accident) are validated with scattered ground-based observations of deposit load and local particle grain size distributions and with measurements from the Radioactivity Environmental Monitoring database. For validation of tephra deposit loads and radionuclides, we use two variants of the normalised root-mean-square error metric. We find that FALL3D-8.0 simulations initialised with data insertion consistently improve agreement with satellite retrievals at all lead times up to 48 h for both volcanic ash and SO2 simulations. In general, SAL scores lower than 1.5 and FMS scores greater than 0.40 indicate acceptable agreement with satellite retrievals of volcanic ash and SO2. In addition, we show very good agreement, across several orders of magnitude, between the model and observat, This research has been supported by the European Commission, H2020 Excellence Science (ChEESE (grant no. 823844)), the European Commission, H2020 Marie Skłodowska-Curie Actions (STARS (grant no. 754433)), the European Commission, H2020 Research Infrastructures (EUROVOLC (grant no. 731070)) and the Ministero dell'Istruzione, dell'Università e della Ricerca (grant no. 805 FOE 2015)., Peer Reviewed, Postprint (published version)

Jan Mayen Island (Norway), located in the North Atlantic, is considered the world"s northernmost active subaerial volcano, with at least five eruptive periods recorded during the last 200 years. Explosive activity of the volcano may seriously affects the nearby important air traffic routes. However, no quantitative studies on the possible impact of a new explosive volcanic eruption on the air traffic have been conducted. In this work, we statistically characterise the spatial and temporal distribution of airborne volcanic ash cloud and its persistence at different flight levels. Since current operational forecast products do not always meet the requirements of the aviation sector and related stakeholders (using coarse time and space scales, with outputs on a 40 km horizontal resolution grid and 6 hour time averages), and they neglect epistemic/aleatory uncertainties in quantitative forecasts on real time, we propose hourly high resolution hazard maps over a 3D-grid covering a 2 km-resolution spatial domain 2000 km x 2000 km wide. We present the use of high-performance computing (HPC) to overcome the computational limitations associated with unbiased long-term probabilistic volcanic hazard assessment (PVHA) .Considering a continuum of possible combinations of Eruptive Source Parameters (ESP) to assess and quantify the uncertainty, and the natural variability associated with wind fields over 20 years of data, from 1999 to 2019, we run thousands of analytical solutions (numerical simulations) using the most recent version of the FALL3D model. As a result, the first comprehensive long-term PVHA for Jan Mayen volcanic island is presented.

This paper presents model validation results for the latest version release of the FALL3D atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. The model validation is based on the new FALL3D-8.0 test suite, which comprises a set of four real case studies that encapsulate the major features of the model; namely, the simulation of long-range fine volcanic ash dispersal, volcanic SO2 dispersal, tephra fallout deposits and the dispersal and deposition of radionuclides. The first two test suite cases (i.e. the June 2011 Puyehue-Cordón Caulle ash cloud and the June 2019 Raikoke SO2 cloud) are validated against geostationary satellite retrievals and demonstrate the new FALL3D data insertion scheme. The metrics used to validate the volcanic ash and SO2 simulations are the structure, amplitude and location (SAL) metric and the figure of merit in space (FMS). The other two test suite cases (i.e. the February 2013 Mt. Etna ash cloud and associated tephra fallout deposit, and the dispersal of radionuclides resulting from the 1986 Chernobyl nuclear accident) are validated with scattered ground-based observations of deposit load and local particle grain size distributions and with measurements from the Radioactivity Environmental Monitoring database. For validation of tephra deposit loads and radionuclides, we use two variants of the normalised root-mean-square error metric. We find that FALL3D-8.0 simulations initialised with data insertion consistently improve agreement with satellite retrievals at all lead times up to 48¿h for both volcanic ash and SO2 simulations. In general, SAL scores lower than 1.5 and FMS scores greater than 0.40 indicate acceptable agreement with satellite retrievals of volcanic ash and SO2. In addition, we show very good agreement, across several orders of magnitude, between the model and observat

We investigate the timescales of the horizontal mass flux decay of wind remobilised volcanic particles in Argentina, associated with the tephra-fallout deposit produced by the 2011–2012 Cordón Caulle (Chile) eruption. Particle removal processes are controlled by complex interactions of meteorological conditions, surface properties and particle depletion with time. We find that ash remobilisation follows a two-phase exponential decay with specific timescales for the initial input of fresh ash (1–74 days) and the following soil stabilisation processes (3–52 months). The characteristic timescales as a function of particle size shows two minimum values, identified for sizes around 2 and 19–37 μm, suggesting that these size-range particles are remobilised more easily, due to the interaction between saltation and suspension-induced processes. We find that in volcanic regions, characterised by a sudden release and a subsequent depletion of particles, the availability of wind-erodible particles plays a major role due to compaction and removal of fine particles. We propose, therefore, a simple and reproducible empirical model to describe the mass flux decay of remobilised ash in a supply-limited environment. This methodology represents an innovative approach to link field measurements of multi-sized and supply-limited deposits with saltation erosion theory., The authors are grateful to Paul Jarvis for his comments and corrections of a previous version of this manuscript as well as his insightful discussions. Sampling collection is part of the National Soil Research Program of INTA. This work was supported by the Swiss National Science Foundation (#200021 – 163152)., Peer Reviewed, Postprint (published version)

This article belongs to the Special Issue Forecasting the Transport of Volcanic Ash in the Atmosphere https://www.mdpi.com/journal/atmosphere/special_issues/volcanic_ash, Resuspension of pyroclastic deposits occurs under specific atmospheric and environmental conditions and typically prolongs and exacerbates the impact associated with the primary emplacement of tephra fallout and pyroclastic density current deposits. An accurate forecasting of the phenomenon, to support Volcanic Ash Advisory Centers (VAACs) and civil aviation management, depends on adapting volcanic ash transport and dispersion models to include specific ash emission schemes. Few studies have attempted to model the mechanisms of emission and transport of windblown volcanic ash, and a systematic study of observed cases has not been carried out yet. This manuscript combines numerical simulations along with a variety of observational data to examine the general features of ash resuspension events in northern Patagonia following the 2011 Cordón Caulle eruption (Chile). The associated outcomes provide new insights into the spatial distribution of sources, frequency of events, transport patterns, seasonal and diurnal variability, and spatio-temporal distribution of airborne ash. A novel modelling approach based on the coupling between Advanced Research core of the Weather Research and Forecasting (WRF-ARW) and FALL3D models is presented, with various model improvements that allow overcoming some limitations in previous ash resuspension studies. Outcomes show the importance of integrating source information based on field measurements (e.g., deposit grain size distribution and particle density). We provide evidence of a strong diurnal and seasonal variability associated with the ash resuspension activity in Patagonia. According to the modelled emission fluxes, ash resuspension activity was found to be significantly more intense during daytime hours. Satellite observations and numerical simulations strongly suggest that major emission sources of resuspended ash were distributed across distal areas (>100 km from the vent) of the Patagonian steppe, covered by a thin layer of f, Leonardo Mingari thanks CONICET for their PhD fellowship. Lucia Dominguez was supported by the Swiss National Science Foundation (project number 200021-63152). The WRF-ARW/FALL3D modelling system has been run on the Marenostrum Supercomputer located in the Barcelona Supercomputer Center (BSC) and an HPC system installed at the National Weather Service (Argentina) with funds from the Argentinian project PIDDEF 41/10. This work has been partially funded by the H2020 Center of Excellence for Exascale in Solid Earth (ChEESE) under the Grant Agreement No. 823844., Peer Reviewed, Postprint (published version)