The HPC Digital Autonomy with RISC-V in Europe (DARE) will invigorate the continent’s High Performance Computing ecosystem by bringing together the technology producers and consumers, developing a RISC-V ecosystem that supports the current and future computing needs, while at the same time enabling European Digital Autonomy. DARE takes a customer-first approach (HPC Centres & Industry) to guide the full stack research and development. DARE leverages a co-design software/hardware approach based on critical HPC applications identified by partners from research, academia, and industry to forge the resulting computing solutions. These computing solutions range from general purpose processors to several accelerators, all utilizing the RISC-V ecosystem and emerging chiplet ecosystem to reduce costs and enable scale. The DARE program defines the full lifecycle from requirements to deployment, with the computing solutions validated by hosting entities, providing the path for European technology from prototype to production systems. The six year time horizon is split into two phases, enabling a DARE plan of action and set of roadmaps to provide the essential ingredients to develop and procure EU Supercomputers in the third phase. DARE defines SMART KPIs for the hardware and software developments in each phase, which act as gateways to unlock the next phase of development. The DARE HPC roadmaps (a living document) are used by the DARE Collaboration Council to maximize exploitation and spillover across all European RISC-V projects. DARE addresses the European HPC market failure by including partners with different levels of HPC maturity with the goal of growing a vibrant European HPC supply chain. DARE Consortium partners have been selected based on the ability to contribute to the DARE value chain, from HPC Users, helping to define all the requirements, to all parts of the hardware development, software development, system integration and subsequent commercialization.

The Copernicus Climate Change Service Evolution (CERISE) project aims to enhance the quality of the C3S reanalysis and seasonal forecast portfolio, with a focus on land-atmosphere coupling. It will support the evolution of C3S by improving the C3S climate reanalysis and seasonal prediction systems and products towards enhanced integrity and coherence of the C3S Earth system Essential Climate Variables. CERISE will develop new and innovative coupled land-atmosphere data assimilation approaches and land initialisation techniques to pave the way for the next generations of the C3S reanalysis and seasonal prediction systems. These developments will include innovative work on observation operators using Artificial Intelligence to ensure optimal data fusion integrated in coupled assimilation systems. They will enhance the exploitation of Earth system observations over land surfaces, including from the Copernicus Sentinels and from the European Space Agency Earth Explorer missions, moving towards an all-sky and all-surface approach. CERISE Research and Innovation will bring the C3S tools beyond the state-of-the-art in the areas of coupled land-atmosphere data assimilation, observation operators, and land initialisation methodologies. CERISE will develop diagnostic tools and prediction skill metrics that include integrated hydrological variables to go beyond the traditional skill scores to assess Earth system coupled reanalysis and seasonal prediction. It will deliver proof-of-concept prototypes and demonstrators, to demonstrate the feasibility of the integration of the developed approaches in the operational C3S. The CERISE outputs aim at medium to long-term upgrades of the C3S systems with targeted progressive implementation in the next three years and beyond. CERISE will improve the quality and consistency of the C3S reanalysis and multi-system seasonal prediction, directly addressing the evolving user needs for improved and more consistent C3S Earth system products.

Aviation is one of the most critical infrastructures of the 21st century. Even comparably short interruptions can cause economic damage summing up to the Billion-Euro range. As evident from the past, aviation shows certain vulnerability with regard to natural hazards. The proposal EUNADICS-AV addresses airborne hazards (environmental emergency scenarios), including volcano eruptions, nuclear accidents and emergencies and other scenarios where aerosols and certain trace gases are injected into the atmosphere. Such events are considered rare, but may have an extremely high impact, as demonstrated during the European Volcanic Ash Crisis in 2010. Before the 1990s, insufficient monitoring as well as limited data analysis capabilities made it difficult to react to and to prepare for certain rare, high-impact events. Meanwhile, there are many data available during crisis situations, and the data analysis technology has improved significantly. However, there is still a significant gap in the Europe-wide availability of real time hazard measurement and monitoring information for airborne hazards describing “what, where, how much” in 3 dimensions, combined with a near-real-time European data analysis and assimilation system. The main objective of EUNADICS-AV is to close this gap in data and information availability, enabling all stakeholders in the aviation system to obtain fast, coherent and consistent information. This would allow a seamless response on a European scale, including ATM, ATC, airline flight dispatching and individual flight planning. In the SESAR 2020 Programme Execution Framework, EUNADICS-AV is a SESAR Enabling project (project delivering SESAR Technological Solutions). The project aims at passing a SESAR maturity level V2, which includes respective service validation activities, including validation exercises. Work will be also done to prepare a full V3 validation.

Supercomputers have been extensively used to solve complex scientific and engineering problems, boosting the capability to design more efficient systems. The pace at which data are generated by scientific experiments and large simulations (e.g., multiphysics, climate, weather forecast, etc.) poses new challenges in terms of capability of efficiently and effectively analysing massive data sets. Artificial Intelligence, and more specifically Machine Learning (ML) and Deep Learning (DL) recently gained momentum for boosting simulations’ speed. ML/DL techniques are part of simulation processes, used to early detect patterns of interests from less accurate simulation results. To address these challenges, the ACROSS project will co-design and develop an HPC, BD, and Artificial Intelligence (AI) convergent platform, supporting applications in the Aeronautics, Climate and Weather, and Energy domains. To this end, ACROSS will leverage on next generation of pre-exascale infrastructures, still being ready for exascale systems, and on effective mechanisms to easily describe and manage complex workflows in these three domains. Energy efficiency will be achieved by massive use of specialized hardware accelerators, monitoring running systems and applying smart mechanisms of scheduling jobs. ACROSS will combine traditional HPC techniques with AI (specifically ML/DL) and BD analytic techniques to enhance the application test case outcomes (e.g., improve the existing operational system for global numerical weather prediction, climate simulations, develop an environment for user-defined in-situ data processing, improve and innovate the existing turbine aero design system, speed up the design process, etc.). The performance of ML/DL will be accelerated by using dedicated hardware devices. ACROSS will promote cooperation with other EU initiatives (e.g., BDVA, EPI) and future EuroHPC projects to foster the adoption of exascale-level computing among test case domain stakeholders.