Abstract
<jats:p>Weather-related disruptions in aviation are inevitable, but their operational impact often depends on how effectively forecast information is translated into decisions. In January 2026, MeteoSwiss launched operationally adMET (Aerodrome Meteorological Forecast Information Service), an ML-enhanced aviation forecasting system designed to strengthen the entire forecast-to-alerting-to-decision chain within airport and air traffic control operations. The system integrates data from high-resolution ensemble Numerical Weather Prediction (NWP) and real-time observations with machine-learning-based predictions to generate calibrated probabilistic guidance from the immediate nowcasting range (0-2 h) up to 30 hours ahead. Increased temporal resolution in the short range and statistical refinement of ensemble output allow the system to provide rapidly updated impact indicators for low visibility, ceiling constraints, wind limitations, and convective activity. Rather than focusing on raw meteorological variables, adMET delivers threshold-based, operationally interpretable information that directly links forecast uncertainty to capacity and safety implications. The transition from development to daily operations revealed that the critical challenge lies not only in forecast skill, but in embedding probabilistic information within established workflows. Air traffic controllers, apron coordinators, and dispatchers operate within tightly constrained decision timelines. For probabilistic forecasts to add value, they must be trusted, intuitively visualized, and clearly connected to operational consequences. Extensive user engagement, iterative interface adjustments, and targeted training were therefore essential components of the implementation process. Early operational feedback indicates tangible benefits across the aviation decision chain. Enhanced short-term calibration supports earlier recognition of potential bottlenecks, smoother coordination among stakeholders, and more consistent management of weather-induced capacity reductions. At the same time, the system maintains human oversight, positioning ML-driven post-processing as an automated, yet transparent, first-guess layer within a broader decision-support framework. This contribution discusses the interdisciplinary lessons learned from embedding machine-learning-based probabilistic forecasting into an operational decision environment. It highlights how the seamless integration of deterministic and probabilistic prediction, combined with user-centered communication strategies, can strengthen the link between advanced forecasting systems and real-world response. This demonstrates how data-driven models can meaningfully contribute to impact-oriented warning processes in high-stakes aviation contexts.</jats:p>