Digital Twins and Digital Twin Engine
The first two high-priority digital twins (DT) are the Weather-Induced Extremes Digital Twin and the Climate Change Adaptation Digital Twin, the Digital Twin Engine is the software infrastructure permitting and enabling their implementation and eventual operation. The data generated by DestinE DTs is included in the DestinE Data Lake and accessible through the Destination Earth Service Platform (DESP), the other two components of Destination Earth.
Towards a Digital Twin Earth
The Climate Change Adaptation Digital Twin (Climate Adaptation DT) and the Weather-Induced Extremes Digital Twin (Extremes DT), provide high quality simulations integrating Earth system information and the sectors most impacted by climate change and extreme weather events, exploring new forms of interactivity. The continuous component of the Extremes DT will provide global kilometre (km) - scale simulation capabilities to assess and predict environmental extremes within a time span of a few days. Both DTs include the impact sector elements to better respond to its users' needs.
The Climate Adaptation DT generates km-scale simulations of climate scenarios from global to regional and national levels at a multi-decadal timescale, including uncertainty quantification. The on-demand component of the Extremes DT provides a configurable capability for an interactive European monitoring and prediction framework at sub-km scale for meteorological, hydrological and air quality extremes.
Powering the DTs, the Digital Twin Engine is the software infrastructure needed for extreme-scale simulations and data fusion, data handling and machine learning, adapted to exploit the capabilities of the EuroHPC supercomputers. It will also provide a flexible environment to operate the digital twins and trial on demand configurations adapted to specific applications.
Expected impact
The digital twins of DestinE will provide users with tailored access to high-quality knowledge for user-specific scenario development that can support evidence-based decision-making.