In the H2020 project “Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM” (SINOPTICA), an air traffic controller support system was extended to organize approaching traffic even under severe weather conditions. During project runtime, traffic days with extreme weather events in the Po Valley were analyzed, an arrival manager was extended with a module for 4D diversion trajectory calculation, two display variants for severe weather conditions in an air traffic controller primary display were developed, and the airport Milano Malpensa was modelled for an air traffic simulation. On the meteorological side, three new forecasting techniques were developed to better nowcast weather events affecting tactical air traffic operations and used to automatically organize arrival traffic. Additionally, short-range weather forecasts with high spatial resolution were elaborated using radar-based nowcasting and a numerical weather prediction model with data assimilation. This nowcast information was integrated into the extended arrival manager for the sequencing and guiding of approaching aircraft even in adverse weather situations. The combination of fast and reliable weather nowcasts with a guidance support system enables severe weather diversion coordination in combination with a visualization of its dynamics on traffic situation displays.

Today, integrating new functions into air traffic controller working positions or developing completely new displays are time-consuming and expensive processes. The users are often only included during the concept phase and after the main development phase is completed. Therefore, they do not have the chance to influence the design and development process by giving structural feedback. Any subsequent changes to the system after completing the main development phase will be expensive and slow. This paper proposes a new approach to integrate designers and users more tightly in the development process for digital air traffic control systems. By creating and reviewing realistic mock-ups in small iterative steps, the look and feel of future support functions can be validated in advance of the actual implementation and easily adapted if changes are requested. We performed a series of steps to evaluate the new workflow as a case study, including idea development, design, validation, and implementation into the target system. In a validation campaign with air traffic controllers, the developed design and functionalities received very positive feedback and the new workflow was successfully applied and evaluated as a case study.

Increasing traffic density and airport surface complexity cause growing demands on air traffic control. Automation, that expands and matures, could help to manage this challenge and reduce air traffic controller workload. In this paper we focus on the automation of air traffic control for ground movements and validate prospects and acceptance of automation implemented by an automated ground movement management system. For this purpose, a prototype of a surface management system was enhanced with a module capable of controlling traffic. The aim of the validation trials was to assess the feasibility and acceptance of the automation concept. This includes the influence of automation on situation awareness, workload and the detection of safety-critical and operational disturbances. For this purpose, real-time human-in-the-loop simulations were conducted with ten ground and apron controllers in DLR’s apron and tower simulator. This paper describes and discusses the addressed research questions, the experimental set-up and the methods used for data collection and data analysis. It includes a comprehensive analysis and an overview of the simulation and debriefing results, providing valuable insights into the participants’ opinions on automation at the ground controller working position. More than half of the participants (56%) agreed that the automated system controlled the traffic safely and orderly. Furthermore, 85% of the intentionally induced disturbances were detected by participants while using the automation. Both concerns and recommendations for further improvement have been identified and analysed.