Mingari, Leonardo

AbstractThe 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.

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 inter- est 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 al- gorithms, including ensemble Kalman filter (EnKF) meth- ods, 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, in- cluding aerosol concentrations and tephra deposit mass load- ing: the Gaussian with non-negative constraints (GNC) and gamma inverse-gamma (GIG) methods. The proposed meth- ods are applied to reconstruct the tephra fallout deposit re- sulting from the 2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conducted consider- ing two independent datasets of deposit thickness measure- ments: an assimilation dataset and a validation dataset. Dif- ferent 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 ob- tained 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 assimila- tion 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 previ- ous studies. On the other hand, although it is an interesting approach, the GIG method failed to improve the EnKF analysis.

Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on infrastructures, 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, 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 satellite-retrieved data of column mass loading. We show that this procedure may lead to nonphysical solutions and, consequently, conclude that LETKF is not the best approach for the assimilation of volcanic aerosols. However, we find that a truncated state constructed from the LETKF solution approaches the real solution after a few assimilation cycles, yielding a dramatic improvement of forecast quality when compared to simulations without assimilation.

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.

This manuscript 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 out to 48 hours 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 observations for the 2013 Mt. Etna and 1986 Chernobyl case studies. Our results, along with the validation datasets provided in the publicly available test suite, form the basis for future improvements to FALL3D (versions 8 or later) and also allow for model inter-comparison studies.

This manuscript presents FALL3D-8.0, the last version release of an open-source code with 15+ years of track record and a growing number of users in the vol- canological and atmospheric communities. The code has been redesigned and rewritten from scratch in the framework of the EU Center of Excellence for Exascale in Solid Earth (ChEESE) in order to overcome legacy issues and allow for successive optimisations that are already planned in the preparation of the code towards extreme-scale computing. However, this baseline version already contains substantial improvements in terms of model physics, solving algorithms, and code accuracy and performance. The code, originally conceived for atmospheric dispersal and deposition of tephra particles, has been extended to model other types of particles, aerosols and radionuclides. The solving strategy has also been changed, replacing the former central-differences scheme for a high-resolution central-upwind scheme derived from finite volumes, which minimises numerical diffusion even in presence of sharp concentration gradients and discontinuities. The parallelisation strategy, Input/Output (I/O), model pre-process workflows and memory management have also been reconsidered, leading to substantial improvements on code scalability, efficiency, and overall capability to han- dle much larger problems. This paper details the FALL3D-8.0 model physics and the numerical implementation of the code.