Digital twin for weather-induced extremes

Our presentation aims to describe the development and operationalisation of the Destination Earth (DestinE) Extremes Digital Twin (DT), including the On-Demand component, a system designed to improve the prediction and management of extreme weather events in Europe. The system leverages high-resolution weather models using information from Extreme Detection (EDF) and Triggering (DTF) Frameworks, as well as ECMWF ensemble, incorporating impact-specific models for hydrology, air quality, renewable energy, and more. A key component is a configuration lookup table prioritising end-user needs and available resources. The system incorporates various masking techniques (ACCORD models configurations, geographical, capacity, event type) to refine forecasts. The presentation describes the system's architecture, data sources, and workflow, emphasising the integration of multiple models and data sources, and the use of cutting-edge technologies such as GPUs and machine learning for enhanced forecasting and efficient resource utilisation. Pilot regions are used for testing and operationalisation, with a phased approach planned for broader deployment. The project addresses challenges in forecasting accuracy, communication of uncertainty, and the integration of forecasts into decision-making processes across various sectors.