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Tsunamis constitute a significant hazard for European coastal populations, and the impact of tsunami events worldwide can extend well beyond the coastal regions directly affected. Understanding the complex mechanisms of tsunami generation, propagation, and inundation, as well as managing the tsunami risk, requires multidisciplinary research and infrastructures that cross national boundaries. Recent decades have seen both great advances in tsunami science and consolidation of the European tsunami research community. A recurring theme has been the need for a sustainable platform for coordinated tsunami community activities and a hub for tsunami services. Following about three years of preparation, in July 2021, the European tsunami community attained the status of Candidate Thematic Core Service (cTCS) within the European Plate Observing System (EPOS) Research Infrastructure. Within a transition period of three years, the Tsunami candidate TCS is anticipated to develop into a fully operational EPOS TCS. We here outline the path taken to reach this point, and the envisaged form of the future EPOS TCS Tsunami. Our cTCS is planned to be organised within four thematic pillars: (1) Support to Tsunami Service Providers, (2) Tsunami Data, (3) Numerical Models, and (4) Hazard and Risk Products. We outline how identified needs in tsunami science and tsunami risk mitigation will be addressed within this structure and how participation within EPOS will become an integration point for community development.

Nell’ambito del Progetto Europeo H2020 “ODYSSEA” (Operating a network of integrateD observatorY Systems in the mediterranean SEA, http://odysseaplatform.eu/) è in corso di sviluppo un sistema modellistico previsionale delle condizioni ambientali (fisiche e biogeochimiche) del Mare Adriatico settentrionale. Il sistema modellistico è costituito dall’accoppiamento “on-line” del modello Europeo di circolazione generale “NEMO” (Nucleus for European Modelling of the Ocean, https://www.nemo-ocean.eu/ ), con il modello biogeochimico marino “BFM” (Biogeochemical Flux Model, bfm-community.eu/). La componente biogeochimica del modello comprende la simulazione dei processi biogeochimici sia della colonna d’acqua che dei sedimenti ed il loro accoppiamento. Il sistema è implementato con una risoluzione orizzontale di circa 800 m ed una risoluzione verticale di 2 m, in coordinate z. Il forzante atmosferico è costituito da previsioni ed analisi provenienti dai centri meteorologici nazionali e internazionali. Gli apporti fluviali di acqua dolce e di sali nutritivi considerano il runoff giornaliero del fiume Po, mentre per gli altri fiumi dell’area di studio vengono considerati valori climatologici. Le condizioni al contorno aperto per la componente fisica del sistema modellistico provengono da Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/ ). Le simulazioni sono condotte sul sistema di classe Tier0 Marconi del CINECA, partizione A2, che utilizza i processori many-cores Intel Knights Landing (KNL) ed ha una potenza di picco di circa 11 Pflops. Viene presentata la metodologia di indagine che è stata utilizzata per massimizzare le prestazioni del sistema modellistico accoppiato NEMO+BFM per il caso del Mare Adriatico Settentrionale sull’architettura di Marconi A2. Vengono illustrati alcuni risultati ottenuti mediante l’uso di opportuni strumenti di profiling/tuning delle prestazioni, relativi a specifiche metriche computazionali (vettorizzazione, scalabilità ad alto numero di cores, etc.). Vengono, infine, presentati i risultati preliminari relativi alla validazione del modello, condotta in “hindcast mode”, confrontando i risultati delle simulazioni con le osservazioni disponibili, ed i primi risultati dei test previsionali.

Domain decomposition of two-dimensional domains on which boundary-value elliptic problems are formulated, is accomplished by probabilistic (Monte Carlo) as well as by quasi-Monte Carlo methods, generating only few interfacial values and interpolating on them. Continuous approximations for the trace of solution are thus obtained, to be used as boundary data for the sub-problems. The numerical treatment can then proceed by standard deterministic algorithms, separately in each of the so-obtained subdomains. Monte Carlo and quasi-Monte Carlo simulations may naturally exploit multiprocessor architectures, leading to parallel computing, as well as the ensuing domain decomposition does. The advantage such as scalability obtained increasing the number of processors is shown, both theoretically and experimentally, in a number of test examples, and the possibility of using clusters of computers (grid computing) emphasized.

The expensive reengineering of the sequential software and the difficult parallel programming are two of the many technical and economic obstacles to the wide use of HPC. We investigate the chance to improve, in a rapid way, the performance of a numerical serial code modelling semiconductor devices, exploiting the parallel features of shared memory achitectures. OpenMP seems to be the good choice in order to guarantee the portability, that is one of the big issues in parallel computing.

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)., Future Generation Computer Systems, 146, ISSN:0167-739X, ISSN:1872-7115

Probabilistic Tsunami Hazard Analysis (PTHA) quantifies the probability of exceeding a specified inundation intensity at a given location within a given time interval. PTHA provides scientific guidance for tsunami risk analysis and risk management, including coastal planning and early warning. Explicit computation of site-specific PTHA, with an adequate discretization of source scenarios combined with high-resolution numerical inundation modelling, has been out of reach with existing models and computing capabilities, with tens to hundreds of thousands of moderately intensive numerical simulations being required for exhaustive uncertainty quantification. In recent years, more efficient GPU-based High-Performance Computing (HPC) facilities, together with efficient GPU-optimized shallow water type models for simulating tsunami inundation, have now made local long-term hazard assessment feasible. A workflow has been developed with three main stages: 1) Site-specific source selection and discretization, 2) Efficient numerical inundation simulation for each scenario using the GPU-based Tsunami-HySEA numerical tsunami propagation and inundation model using a system of nested topo-bathymetric grids, and 3) Hazard aggregation. We apply this site-specific PTHA workflow here to Catania, Sicily, for tsunamigenic earthquake sources in the Mediterranean. We illustrate the workflows of the PTHA as implemented for High-Performance Computing applications, including preliminary simulations carried out on intermediate scale GPU clusters. We show how the local hazard analysis conducted here produces a more fine-grained assessment than is possible with a regional assessment. However, the new local PTHA indicates somewhat lower probabilities of exceedance for higher maximum inundation heights than the available regional PTHA. The local hazard analysis takes into account small-scale tsunami inundation features and non-linearity which the regional-scale assessment does not incorporate. However, the deterministic inundation simulations neglect some uncertainties stemming from the simplified source treatment and tsunami modelling that are embedded in the regional stochastic approach to inundation height estimation. Further research is needed to quantify the uncertainty associated with numerical inundation modelling and to properly propagate it onto the hazard results, to fully exploit the potential of site-specific hazard assessment based on massive simulations.