PROB4LOWW - Probabilistic MET Information for Capacity Optimisation in Arrival and Departure Management
Weather phenomena such as thunderstorms, strong winds and fog are responsible for 80-90% of all delays at Vienna Airport. However, providing forecasts of these weather events at the spatial and temporal accuracy required by the air traffic management (ATM) system is generally not possible.
This has to do with the very nature of weather phenomena, which, because of their small scale and high temporal variability, do not permit precise forecasting. Due to this apparent shortcoming, the estimation of capacities in arrival and departure management still relies on the subjective judgment of flight planners and approach supervisors. Uncertainties, inherent in all weather information, are thus insufficiently taken into account in ATM decisions.
To provide evidence that the use of probabilistic weather forecasts for arrival and departure management is both worthwhile and feasible, and that the resulting benefits can be suitably quantified.
Probabilistic weather predictions aim to incorporate forecast uncertainties using probabilities for the occurrence of a relevant weather event. In the proposed exploratory project, a concept for the integration of probabilistic meteorological information in arrival and departure management for capacity optimization shall be devised. Experts in aviation operations, flight planning, ATM, air traffic simulation, and aviation meteorology will identify and analyse all weather-related ATM decisions.
Flight planning and operation guidelines as well as detailed simulations of air traffic will be used to determine the costs incurred at the occurrence of a given weather event ("loss") and to compare them with the costs of the protective actions against the event's impact. In the framework of economic decision models, the so-obtained cost-loss ratio is then used to determine the optimal probability thresholds required to set arrival and departure rates at the occurrence of individual weather events.
With this multi-disciplinary and cost-based approach, probabilistic weather information will be translated into an integrated and deterministic decision-making process beneficially in order to reduce flight delays, improve predictability and ease the planning and managing workload of air traffic controllers.
The use of probability information underlines the fact that decisions, made on the basis of uncertain input information, are found to be incorrect at times. To mitigate this unavoidable risk, an adaptive decision-support procedure will be devised, refining the forecast information and any measure derived from it with the current state of the weather.
It has been proven that the use of probabilistic weather forecasts in arrival and departure management is both worthwhile and feasible, and that its benefits can be suitably quantified.