## 3 Results
### Meteorological Forecasting and Assimilation Performances
#### 3.1.1 Irrigation-Focused Demonstrators  
The Romanian and Italian demonstrators were designed to improve irrigation management by incorporating data assimilation techniques into weather forecasting. In the Romanian demonstrator, the data assimilation process involved assimilating 2 mT observations from the national network, radar reflectivity, ZTD data from GNSS stations, and Meteodrone data when available. The same data were assimilated in the Italian demonstrator, except for Meteodrone data, which were not available during the study period. The results obtained from a meteorological and assimilation perspective were similar at both sites; therefore, the outcomes are presented jointly in the following discussion. As a first evaluation, the bias and standard deviation values for 2 mT, ZTD, and radar reflectivity were analyzed across the three assimilation cycles, providing insights into forecast error propagation throughout the assimilation process (Appendix A.2). To provide an overall assessment of the impact of data assimilation in the Romanian demonstrator, Figure 5 summarizes the performance of key variables by comparing the background forecast (BF) and the analysis (DA) against observations. The evaluation focuses on three variables: 2-m temperature (2 mT), radar reflectivity, and GNSS-derived Zenith Total Delay (ZTD), analyzed across multiple assimilation cycles. For near-surface temperature, the results show a clear and substantial improvement after assimilation. Both the standard deviation and bias of 2 mT (Figure 5A,B) residuals are significantly reduced in the analysis compared to the background forecast, confirming that assimilation effectively corrects temperature errors and stabilizes the forecast near the surface. In the case of radar reflectivity (Figure 5C,D), the impact of assimilation is more moderate. The analysis shows a slight reduction in bias, indicating that the assimilation process helps to nudge the model towards better agreement with observed convective structures. However, the standard deviation remains largely unchanged. This behavior is expected, considering the high inherent variability and noise of radar observations, as well as the limited number of cases available, which constrain the potential for noticeable improvements in the spread of errors. Regarding GNSS-ZTD (Figure 5E,F), assimilation leads to a slight reduction in bias, bringing the model's representation of atmospheric water vapor closer to observations. However, the improvement remains within the observational error margin of approximately 1.5 cm, which is already accounted for in the 3D-Var assimilation scheme. Moreover,  
Figure 5: Boxplots showing the standard deviation (SD) and bias of residuals for 2-meter temperature (2 mT, **A**,**B**), radar reflectivity (**C**,**D**), and GNSS-derived Zenith Total Delay (ZTD, **E**,**F**) over multiple assimilation cycles in the Romanian demonstrator. Results compare the background forecast (obs-background) and the analysis (obs-analysis) residuals against observations. since the analyzed period did not include particularly intense meteorological events, the background forecast already provided an adequate representation of the vapor field, thus limiting the potential for further correction through assimilation. In the Romanian demonstrator, vertical profiles collected by weather drones (Meteodrones) were assimilated whenever available, with the objective of assessing their contribution to improving the model's representation of key atmospheric variables such as temperature, dew point temperature, wind speed, and wind direction. In total, eight assimilation cycles involving drone data were analyzed, corresponding to eight distinct time instances under different meteorological conditions. These profiles provided high-resolution information in the lower and middle troposphere, where traditional observations are typically sparse, making them particularly valuable for short-term forecast improvements. For an evaluation of the impact of cycling assimilation, see Appendix A.1.