Unmanned aerial vehicles have become essential tools in a wide range of applications. As drone operations grow more complex, the accurate prediction of battery runtime and aerodynamic flight safety risks, particularly those caused by wind, becomes increasingly important. This study employs the Wind Resistance Risk Index (WRRI), to quantify the impact of wind conditions on UAV performance. While several predictive models have been introduced to address these issues, many have not been thoroughly validated under real operational conditions. This study focuses on the post-validation of a previously developed fuzzy-based predictive model, using telemetry data collected from four UAV missions. Key flights and battery parameters were analyzed. The results demonstrate that real-world flight data provide valuable insight into model reliability and highlight discrepancies that can guide future model refinement. This work contributes to enhancing UAV safety by bridging the gap between theoretical predictions and empirical evaluations, specifically under varying wind conditions.