The SINOPTICA project aims at exploiting the untapped potential of assimilating remote sensing (EO-derived and ground-based radar) as well GNSS-derived datasets and in situ weather stations data into very high-resolution, very short-range numerical weather forecasts to provide improved prediction of extreme weather events to the benefit of ATM operations. This will be done by setting up a continuously updated database of remote sensing-derived, GNSS-derived and in situ weather stations variables, in combination with an automated assimilation system to feed an NWM. The usefulness of deploying dedicated networks of sensors to monitor atmospheric variables at high spatial resolution in the vicinity of ATM "hotspots" such as airports will be investigated as well. SINOPTICA weather forecast results will be integrated into ATM decision-support tools, visualizing weather information on the controller's display, and generating new 4D trajectories to avoid severe weather areas. The usefulness of the newly developed SI

The MAGDA project aims at developing a toolchain for atmosphere monitoring, weather forecasting, and severe weather/irrigation/crop monitoring advisory, with GNSS (including Galileo) at its core, to provide useful information to agricultural operators. MAGDA will exploit the untapped potential of assimilating GNSS-derived, drone-derived, Copernicus EO-derived datasets, in situ weather sensors into very high-resolution, short-range (1-2 days ahead) and very short-range (less than 1 day ahead) numerical weather forecasts to provide improved prediction of severe weather events (rainfall, snow, hail, wind, heat and cold waves) as well as of weather-driven agriculture pests and diseases to the benefit of agriculture operations, also in light of ongoing effects of climate change. These targets will be achieved by setting up a database of variables of interest, and an assimilation system to feed a numerical weather prediction model, which in turn drives a hydrological model for irrigation performance and water accounting to assess water use and related productivity. In addition to already existing observational networks, new dedicated networks of sensors, including GNSS and drones, to monitor atmospheric variables at high spatial resolution will be deployed in the vicinity of large farms and cultivated areas, to provide data with high spatial and temporal resolutions for the assimilation into the weather model. The delivery of the augmented forecasts and irrigation advisories to farmers will be enabled by a dedicated dashboard and APIs to already existing Farm Management Systems. The tools developed within MAGDA will represent the technical and methodological components based on which services to support agricultural operations will be defined.