# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 3 AMAN Air Traffic Controller Support  
Arrival Manager (AMAN) systems have been developed and deployed in Europe over the course of the last 25 years. They are primarily designed to provide automated sequencing support for ATCOs handling traffic arriving at an airport by continuously calculating arrival sequences and times for flights while considering the locally defined landing rate, the required spacing for flights arriving to the runway, and other criteria. The AMAN has to generate flyable 4D trajectories with target times for all significant waypoints, including runway thresholds.  
During the last twenty-five years, the DLR's Institute of Flight Guidance in Braun-schweig has developed arrival management systems for different kinds of scientific applications at various international airports. The latest version of DLR's previously developed arrival manager tools \"COMPAS\" [28] and \"4D-Planner\" [29] is called the 4-dimensional Cooperative Arrival Manager (4D-CARMA). Both previous versions were the result of research projects in close cooperation with the Deutsche Flugsicherung GmbH (DFS). Considering different constraints, such as weight classes, runway separation criteria, or runway allocation, 4D-CARMA uses radar data and additional information, such as the flight plans of all arriving aircraft, for sequencing and trajectory calculation. AMANs are pure suggestion systems and have a planning horizon of around one hour. They ease ATCOs' tasks by taking over the particularly difficult planning and optimization of approach sequences while considering all given constraints. This technical support in approach planning can have a clearly positive influence on the effectiveness of ATCOs' work since approaching aircraft are integrated at an early stage, the required distances are precisely considered, throughput is slightly increased, and approach trajectories are more direct and thus shorter [30]. This is especially true when the decision support systems are combined with speech recognition [31]. In recent years, developments in Arrival Managers have gone in two main directions: On the one hand, the planning horizon has been systematically extended to several hundred miles, giving aircraft a precise target time for the threshold and, thus, a position in the landing sequence already in the en-route phase [32]. On the other hand, attempts are being made to use machine learning (ML) methods to build pilot support systems that are based on trajectories that have actually been flown and can thus better represent the typical actions of controllers and pilots in certain situations than classic deterministic algorithms [33; 34]. With the help of ML, attempts are also being made to support more environmentally friendly approach planning and thus further reduce the environmental impact of aviation [35].  
The first arrival managers already developed the systematic base for ATCO support, and this has not changed in principle. Accordingly, the tasks of an AMAN can be divided into different levels: \"Sequence Planning\" calculates optimal landing sequences based on airspace structure, current air traffic situations, and performance criteria for all aircraft in the airspace. \"Trajectory Calculation\" creates optimal 4D routes for every individual aircraft to fulfil the planned sequence. \"Advisory Generation\" deduces the required instructions from air traffic controller to pilot to follow the calculated trajectory, and \"Conformance Monitoring\" tracks if aircraft follow the planned trajectories. In order to support adverse weather avoidance, the assistant functionalities of an AMAN have to be adapted and new assistant functionalities need to be provided. The most important elements of an AMAN capable of supporting severe weather area diversions are conflict detection between routes and weather areas, alternate route finding, sequence calculation, and trajectory generation.