What are the air traffic management (ATM) areas that can benefit from Met data? Can the stability and predictability of ATM systems be improved by appropriately using Met uncertainty information in optimisation and decision making processes?
The IMET project aims to develop probabilistic trajectory prediction (PTP) using research systems and demonstrate the benefits on research TP systems. However, the approach will be designed to be generic so as to be applicable to operational/industrial TPs as well.
The expected results are related to pre-tactical flight planning applications in ATM systems. For these applications, existing Met systems generating probabilistic forecasts are used: these systems are known as Ensemble Prediction Systems (EPS). The EPS computations involve repeated calculations using perturbed data, and are therefore computationally intensive, and as a result require additional time to produce their data in comparison with current deterministic modelling.
IMET will assess the optimal approach to use Met uncertainty information in future TP systems. It will also determine key ATM areas that may benefit from the inclusion of Met uncertainty information in the TP-based decision support tools. Furthermore, the project will assess the sensitivity of deterministic TP to the Met input uncertainty and develop methods of extending deterministic TP algorithms into probabilistic TP algorithms. Finally, IMET will optimise probabilistic TP algorithms with respect to TP performance parameters (e.g. avoiding areas affected by convective weather) and investigate optimum choices of Met ensemble weights based on users’ requirements on TP output uncertainty.
The IMET project will thus enable us to determine an optimal approach for incorporating Met uncertainty into future TP systems. This is a challenging topic since the development of an optimal approach for the use of Met in future TP systems requires both a detailed understanding of the Met forecasts, a detailed understanding of TP systems, and access to relevant parts of the TP algorithms. Our awareness of this issue has driven the development of our consortium.