Pilot Services shall meet identified user needs and demonstrate complementarity and added value vis-à-vis currently available data or national and European services (e.g. provided by national meteorological services or Copernicus services). Pilot Services shall be delivered for different impact sectors. To this end, contracts are have been placed in: Pilot Services in the energy sector; Pilot Services supporting weather-related extremes resilience and impact mitigation; Pilot Services in the agricultural sector and food security; Pilot Services for environmental or urban planning. The objective of Contract DE_374a is to co-design a Pilot Service tailored towards dynamic line rating (DLR) computations. The Pilot Service builds upon innovative research performed within the Destination Earth On-Demand Extremes Digital Twin contract (DE_330), which yields the possibility to provide high resolution weather predictions. Through a user-centric co-design approach, working closely and in concert with a Transmission System Operator (TSO), the objective of this contract is to demonstrate how this precise weather data can enhance overhead line ratings in real-time operations as well as in planning grid operations. The objective of Contract DE_374b is to implement DestinE pilot services (Lot 1) or ML-based demonstrators (Lot 2) for selected impact sectors in the energy sector, supporting weather-related extremes resilience and impact mitigation, the agricultural sector and food security, environmental or urban planning, environmental migration or other sectors. The Contractor will set up a pilot service producing flood indicators for the territory of Italy based on their existing national flood forecasting model and Extremes DT data. The principal output will be an estimate of the flood return period based on precipitation estimates, which will be communicated to Italian civil protection authorities. Their assessment of added-value will help co-develop the pilot service. The objective of Contract DE_374c is to propose a high-resolution (1 km) wave forecast system for the Barents Sea, using Extreme DT wind forcing, to improve safety and operational planning beyond current state-of-the-art models. This will enable on-demand forecasts during extreme events to support offshore decision-making and reduce operational risks. The objective of the Contract DE_374d is to develop three pilot services under Phase 2 of DestinE, focused on leveraging Extreme DT output data. It includes the implementation of a global tide and surge forecasting service, a compound flood forecasting service for the Philippines, and a global ship route optimization service. The subject of contract DE-374f is to develop a service demonstrator to help cities mitigate the risks of extreme heat and its impacts on public health, productivity, and urban infrastructure. The contract builds on VITO’s UrbSIM platform, delivering high-resolution data that can inform urban climate adaptation strategies. The objective of Contract is DE_374g is to support climate adaptation for perennial crops—starting with apples and grapes—by providing high-resolution, user-friendly climate information through co-designed tools, leveraging the DestinE components, including the Climate DT, DESP and DEDL. | AI/ML-based demonstrators will develop compelling examples of the DestinE System capabilities, notably DT data and access mechanisms, being exploited by innovative ML-based techniques to enable a step-change in terms in user benefit in any sector. Three contracts were awarded covering impact sectors in Weather-related extremes resilience and impact mitigation and Energy. The objective of Contract DE_374h is to develop and implement a machine learning-based system to integrate multiple forecast model outputs into a single, optimised product. The key objective is to improve the accuracy and reliability of forecasts for critical near-surface parameters (temperature, humidity, wind). The objective of Contract DE_374e is to support the development of a Machine Learning (ML) Demonstrator with two primary objectives. First, it contributes to the DestinE co-design process by delivering a representative demonstrator showcasing the use of advanced ML applications for key impact sectors, supported by a strong community engagement and communication strategy. Second, it aims to empower the user community with the tools, methodologies, and knowledge required to meet their mandates for ensuring a safe, reliable, and clean energy supply across Europe—aligned with Nationally Determined Contributions under the Paris Agreement and the EU’s “Fit for 55” climate targets. The objective of Contract DE_374i is to set up a Machine-Learning (ML) based demonstrator that exploits DestinE Digital Twin precipitation data for improving flood predictions in the Netherlands, targeting users in water authorities, DRM agencies, and municipalities. The approach blends forecasts and nowcasts from different sources and includes a comprehensive assessment of uncertainties via ensemble methods. Results will be integrated in the DestinE Platform via an established front-end application (Geoweb).