## 3 Results  
### Meteorological Forecasting and Assimilation Performances  
A preliminary analysis was conducted to evaluate the model's performance and determine the optimal setup to be applied in the demonstrator phase. Specifically, six case studies (two for each site of interest) were selected to assess the impact of data assimilation under different meteorological conditions. For each case, forecast model runs were validated against radar data corrected with rain gauge measurements, and rainfall verification was performed over various accumulation periods--6 h, 12 h, and 24 h--depending on the event type and agricultural needs. The MODE software (v. 9.1) was used to identify and match observed and forecasted precipitation patterns, followed by the calculation of relevant statistical indices. This preliminary analysis enabled the identification of the most suitable model configuration for subsequent application in the demonstrator phase. The results confirmed that data assimilation significantly improves the accuracy of short-term precipitation forecasts, particularly in terms of spatial alignment and peak intensity, while still providing a positive, though reduced, effect on 24-h forecasts. This supports its operational relevance. This section presents only the results from the demonstrator phase, which leveraged the validated setup to highlight the overall added value of the forecasting approach developed within the project. Two distinct demonstrator types were implemented, each addressing specific practical applications. The first focused on irrigation advice, aiming to improve water management in agriculture by enhancing forecasts of key variables such as temperature, humidity, and rainfall--critical parameters for informed irrigation decisions and crop health. The second demonstrator targeted forecasting of extreme weather events, particularly localized convective storms such as summer thunderstorms. Given their short duration, high intensity, and spatial variability, these events pose significant forecasting challenges. In particular, the French demonstrator applied advanced data assimilation techniques to improve the short-term prediction of convective storms, thereby supporting disaster management and early warning systems. In both demonstrators, all available sensor data from various sources were assimilated to maximize forecast reliability and operational relevance. Within the MAGDA framework, which integrates both irrigation management and extreme weather forecasting demonstrators, different validation strategies were adopted according to the objectives of each case. For the irrigation-focused demonstrators, rainfall validation was delegated to the hydrological component, as the hydrological and irrigation models provide a direct assessment of how rainfall localization and intensity affect water resource management. This approach aligns with previous studies, where hydrological output was used as an indirect validation tool to assess the impact of meteorological forecasts on outcomes relevant to end users [45; 54]. Accordingly, validation in these demonstrators focuses on the contribution of data assimilation, particularly from project-specific sensors integrated into existing networks, to improved model state and forecast performance for agriculturally relevant variables during the summer season. Conversely, for the French demonstrator, which specifically targets short-term forecasting of summer convective events in a nowcasting framework, a direct validation of precipitation forecasts was performed. This approach was intended to highlight the value of data assimilation in improving convective event predictions, where its impact is most relevant for timely risk mitigation. During the demonstrator phase, specific periods and events were selected across the three pilot sites to evaluate the impact of data assimilation under operational conditions. For the Italian site, one time window from 20 to 24 June 2024 was analyzed, focusing on hydrological and irrigation advisory services.