###### Abstract  
Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteoromes, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteoromes to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA's integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. water balance models [31; 32], and machine learning [15; 16; 33], offering a combined approach to efficient irrigation planning. Each of these methodologies presents specific advantages and limitations, requiring a combined approach to maximize efficiency in irrigation planning, as demonstrated in the MOSES project [34], and highlighting the need for systems that can more accurately forecast high-impact weather events affecting crops [35; 36; 37]. In this context, the MAGDA (Meteorological Assimilation from Galileo and Drones for Agriculture) project delivers a highly innovative approach to precision irrigation, addressing key challenges in climate-resilient agriculture. By integrating real-time atmospheric data from GNSS signals, drone-based meteorological profiling, Copernicus Earth Observation data, ground-based radar, and in situ sensors, MAGDA creates a dynamic, data-driven framework for weather and irrigation forecasting. The system significantly improves short- and very short-term predictions, enabling farm-specific adaptive water management strategies. Using cost-efficient GNSS stations and Meteorones for high-resolution atmospheric profiling, MAGDA overcomes limitations of defining initial conditions for numerical weather prediction models and contributes to enhancing weather forecasts. Its irrigation advisory service integrates the SPHY hydrological model, offering accurate water balance simulations, while severe weather forecasts are generated using high-resolution numerical weather prediction models assimilating various types of observations. Furthermore, MAGDA delivers its insights through developer-friendly APIs for seamless integration into Farm Management Systems, making advanced, hyper-local weather and irrigation data accessible and actionable for end-users. Through this combination of multi-source data assimilation, predictive modeling, and user-oriented services, MAGDA provides a novel, operational solution that enhances resilience and efficiency in agricultural water management.