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Today, each flight is filed as a static route not later than one hour before departure. From there on, changes of the lateral route initiated by the pilot are only possible with air traffic control clearance and in the minority. Thus, the initially optimized trajectory of the flight plan is flown, although the optimization may already be based upon outdated weather data at take-off. Global weather data as those modeled by the Global Forecast System do, however, contain hints on forecast uncertainties itself, which is quantified by considering so-called ensemble forecast data. In this study, the variability in these weather parameter uncertainties is analyzed, before the trajectory optimization model TOMATO is applied to single trajectories considering the previously quantified uncertainties. TOMATO generates, based on the set of input data as provided by the ensembles, a 3D corridor encasing all resulting optimized trajectories. Assuming that this corridor is filed in addition to the initial flight plan, the optimum trajectory can be updated even during flight, as soon as updated weather forecasts are available. In return and as a compromise, flights would have to stay within the corridor to provide planning stability for Air Traffic Management compared to full free in-flight optimization. Although the corridor restricts the re-optimized trajectory, fuel savings of up to 1.1 %, compared to the initially filed flight, could be shown.
Martin Lindner; Judith Rosenow; Thomas Zeh; Hartmut Fricke. In-Flight Aircraft Trajectory Optimization within Corridors Defined by Ensemble Weather Forecasts. Aerospace 2020, 7, 144 .
AMA StyleMartin Lindner, Judith Rosenow, Thomas Zeh, Hartmut Fricke. In-Flight Aircraft Trajectory Optimization within Corridors Defined by Ensemble Weather Forecasts. Aerospace. 2020; 7 (10):144.
Chicago/Turabian StyleMartin Lindner; Judith Rosenow; Thomas Zeh; Hartmut Fricke. 2020. "In-Flight Aircraft Trajectory Optimization within Corridors Defined by Ensemble Weather Forecasts." Aerospace 7, no. 10: 144.
The concept of 4D trajectory management relies on the prediction of aircraft trajectories in time and space. Due to changes in atmospheric conditions and complexity of the air traffic itself, the reliable prediction of system states is an ongoing challenge. The emerging uncertainties have to be modeled properly and considered in decision support tools for efficient air traffic flow management. Therefore, the subjacent causes for uncertainties, their effects on the aircraft trajectory and their dependencies to each other must be understood in detail. Besides the atmospheric conditions as the main external cause, the aircraft itself induces uncertainties to its trajectory. In this study, a cause-and-effect model is introduced, which deals with multiple interdependent uncertainties with different stochastic behavior and their impact on trajectory prediction. The approach is applied to typical uncertainties in trajectory prediction, such as the actual take-off mass, non-constant true air speeds, and uncertain weather conditions. The continuous climb profiles of those disturbed trajectories are successfully predicted. In general, our approach is applicable to all sources of quantifiable interdependent uncertainties. Therewith, ground-based trajectory prediction can be improved and a successful implementation of trajectory-based operations in the European air traffic system can be advanced.
Thomas Zeh; Judith Rosenow; Hartmut Fricke. Interdependent Uncertainty Handling in Trajectory Prediction. Aerospace 2019, 6, 15 .
AMA StyleThomas Zeh, Judith Rosenow, Hartmut Fricke. Interdependent Uncertainty Handling in Trajectory Prediction. Aerospace. 2019; 6 (2):15.
Chicago/Turabian StyleThomas Zeh; Judith Rosenow; Hartmut Fricke. 2019. "Interdependent Uncertainty Handling in Trajectory Prediction." Aerospace 6, no. 2: 15.