In this project the problem of analysing and quantifying the effects of meteorological uncertainty in Trajectory Based Operations is addressed. In particular, two problems are considered: 1) trajectory planning and 2) sector demand analysis, both at the pre-tactical level (up to three hours before departure) and tactical level (during the flight). In each problem two types of meteorological uncertainty are considered: wind uncertainty and convective zones (including individual storm cells). Weather predictions will be based on Ensemble Prediction Systems and Nowcasts. At the trajectory scale, the main objective is to assess and improve the predictability of efficient 4D trajectories when weather uncertainty is taken into account. To reach this goal, a methodology based on the use of stochastic optimal control algorithms will be explored for robust trajectory planning at the pre-tactical level. At the tactical level, various tactics will be investigated to avoid storms by using a Monte-Carlo method. At the sector scale, the main objective is to analyse the impact of the previously developed trajectory planning on sector demand. To achieve this objective, a methodology will be developed to measure the uncertainty of sector demand (probabilistic sector loading) based on the uncertainty of the individual trajectories. This analysis will also provide an understanding of how weather uncertainty propagates from the trajectory scale to the sector scale. All the solutions proposed in this project will be evaluated and assessed using an advanced air traffic simulator. This project is fully aligned with the call, where the following objectives are stated: “to enhance meteorological capabilities and their integration into ATM planning processes for improving ATM efficiency” and “to develop 4D trajectories that are optimised to take account of all environmental considerations”.

This project addresses the topic “Environment and Meteorology for ATM”. The framework for this project is the integration of meteorological forecast uncertainty information into the decision-making process for Flow Management Position (FMP). FMP is an operational position located in Area Control Centres (ACC) which serves as an interface between Air Traffic Control (ATC) and the Network Manager (NM) Operations Centre. FMP monitors the level of traffic in ATC sectors, adjusts the value of capacity in view of unexpected events, and coordinates possible traffic flow measures with the ACC Supervisor and the NM when an excess of demand over capacity is detected. The presence of storms challenges ATC: it makes the sector demand not easy to predict and increases the complexity, thus reducing the sector capacity. The overall objective of FMP-Met is to provide the FMP with an intuitive and interpretable probabilistic assessment of the impact of convective weather on the operations, up to 8 hours in advance, coming from the combination of the probabilistic sector demand, complexity and capacity reduction, to allow better-informed decision making. FMP-Met has the following specific objectives: Tailor multi-scale, multi-source convective weather information for FMP application; forecast multi-sector demand and complexity under convective weather; translate convective weather forecasts into predictions of reduced airspace capacity; and produce guidelines on the use of probabilistic forecasts for FMP application. The expected impact of this project is the enhancement of ATM efficiency by improving decision making in traffic flow management under convective weather. The provision of a trustworthy forecast of the future sector demand and of a reliable estimation of the impact of the convective weather in the sector capacity will support the FMP in taking anticipated, appropriate, and timely tactical flow measures, which as a consequence will lead to a reduction of delays.

ISOBAR aims at the provision of a service- and AI-based Network Operations Plan, by integrating enhanced convective weather forecasts for predicting imbalances between capacity and demand and exploiting AI to select mitigation measures at local and network level in a collaborative ATFCM operations paradigm. To achieve this vision, four objectives are set: a) Reinforce collaborative ATFCM processes at pre-tactical and tactical levels into the LTM (local) and Network Management (network) roles integrating dynamic weather cells. b) Characterisation of demand and capacity imbalances at pre-tactical level [-1D, -30min] depending on the input of probabilistic weather cells by using applied AI methods and ATM and weather data integration. c) User-driven mitigation plan considering AUs priorities (and fluctuations in demand based on weather forecasts) and predicted effectiveness of ATFCM regulations, considering flow constraints and network effects. d) Develop an operational and technical roadmap for the integration of ancillary services (providing AI-based hotspot detection and adaptative mitigation measures) into the NM platform, by defining interfaces, functional and performance requirements.