Operational data pipelines for aviation weather decision support: the adMET system at MeteoSwiss

Air traffic management requires timely and reliable weather information to support decision-making at airports. To address this need, MeteoSwiss has developed adMET (Aerodrome Meteorological Forecast Information Service), an operational system that integrates meteorological forecast data and post-processing workflows to provide tailored weather guidance for air traffic controllers and other aviation stakeholders. The system currently supports the major Swiss airports of Zurich (ZRH) and Geneva (GVA).

The adMET workflow combines heterogeneous meteorological datasets, including numerical weather prediction output and derived forecast products, and transforms them into aviation-relevant information on wind, visibility, thunderstorms, temperature, pressure, and precipitation. Python-based processing pipelines are used to select suitable forecast sources and apply post-processing steps that transform raw model output into operationally useful products. Examples include spatial aggregation around airports and polar representations of vector quantities such as wind. These transformations allow aviation users to interpret forecast information directly in the context of arrival and departure planning. From a technical perspective, the system is implemented as a modular, containerized workflow deployed on OpenShift with additional showcase services running on AWS (Amazon Web Services). Data processing is driven by eventing to ensure optimal timeliness. Individual processing components are orchestrated through automated CI/CD pipelines and executed as independent jobs, enabling flexible deployment, scaling, and maintenance. Monitoring and operational oversight are implemented through cloud-native observability tools, including Prometheus-based metrics collection and Grafana/AWS CloudWatch dashboards. Data products are distributed through a standardized data exchange infrastructure, allowing seamless integration with external aviation stakeholders such as air navigation service providers.

The system demonstrates how modern software engineering practices - including reproducible workflows, containerized applications, and automated deployment pipelines - can support the operationalization of meteorological data processing. By bridging meteorological forecasting systems and aviation decision-support workflows, adMET illustrates how scalable Python-based infrastructures can enhance the usability and operational value of weather data for high-impact applications such as aviation.