## 3 Results
### Meteorological Forecasting and Assimilation Performances
#### 3.1.2 Extreme Event Forecasting: Convective Storms  
At each site, multiple model runs were performed both with and without data assimilation to evaluate its potential added value in providing farmers with more accurate and precise forecasts. To achieve this, a sensitivity analysis was conducted by adjusting specific parameters within the MODE software to determine the optimal configuration for comparing predicted and observed weather patterns. The French demonstrator assimilated data from various sources, including 2 m temperature, Zenith Total Delay (ZTD), and Meteorone observations, to enhance the prediction of convective storms. The assimilation of these observations led to significant improvements in spatial and temporal accuracy, especially regarding the intensity and location of rainfall. By comparing simulations with and without data assimilation, it was found that assimilation improved the model's ability to predict the evolution of convection, even in cases where storms were not particularly severe. The improved forecasts were particularly valuable for localized storm prediction, as they allowed for more accurate storm tracking and better-informed decision-making regarding disaster response and preparedness. For the two French cases (18 June 2024 and 19 June 2024), the evaluation focused on the effect of assimilation on convective storm forecasts in the vicinity of the area of interest. These simulations were run in a nowcasting setup, with shorter forecasts initialized closer to the event. For both events, the verification timestep ranged from 12:00 to 24:00 UTC. It is worth noting that in both cases, the rainfall events were neither particularly intense nor severe, and the recorded precipitation amounts were relatively low: these two days were selected because thunderstorms coincided with the availability of drone data for assimilation. The analysis focused on lower precipitation thresholds, not exceeding 15 mm. This choice was made because setting a higher threshold would have limited the identification of sufficiently relevant objects in the verification process, thereby reducing the effectiveness of the evaluation. In the first French case (18 June 2024), a threshold of 5 mm/12 h was adopted due to the low event intensity. Figure 7 (top) shows the objects recognized by MODE--both observed and forecasted. The left columns display WRF OL objects, while the right columns show WRF DA objects, for the 5 mm/12 h threshold. Blue contours represent observed objects; brown areas indicate forecasted objects; the red dot marks the Beaune site. Data assimilation notably improved the model's performance, especially in terms of spatial pattern alignment, across all evaluated precipitation thresholds. An examination of the statistical indices reveals that DA achieves a slightly higher Critical Success Index (CSI) than OL, indicating a better match between predicted and observed events. CSI excludes correct negatives and focuses solely on relevant forecasts, being sensitive to hits and penalizing both missed events and false alarms without distinguishing the source of error. Both OL and DA exhibit a general overestimation of precipitation; however, DA slightly amplifies this tendency, as reflected by a higher FBIAS. This also impacts the POD index, with DA showing a higher value than OL--indicating that a greater fraction of observed events is correctly predicted. Additionally, DA presents a slightly lower FAR, indicating fewer false alarms. Finally, the bottom panels of Figure 7 display the main MODE-derived attributes, comparing WRF OL (blue bars) and WRF DA (green bars). These confirm the superior spatial consistency of the DA run. More specifically, the attribute \"angle diff\", which measures the difference in orientation between forecasted and observed objects, is smaller in the DA run, indicating better alignment.