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TBO-Met (Meteorological Uncertainty Management for Trajectory Based Operations) A SESAR H2020 Exploratory Research Project Funded by TBO-Met Consortium TBO-Met Consortium: UNIVERSITY OF SEVILLE (USE, Coordinator), METEOSOLUTIONS GmbH (MetSol), PARIS-LODRON-UNIVERSITAT SALZBURG (PLUS), AEMET, and UNIVERSIDAD CARLOS III DE MADRID (UC3M) Contact: Dami´ an Rivas (USE, Project coordinator), Juergen Lang (MetSol), Carl-Herbert Rokitansky (PLUS), Juan Simarro (AEMET), and Manuel Soler (UC3M) Website: https://tbomet-h2020.com Abstract In TBO-Met 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) trajec- tory 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 in- dividual 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 tra- jectories 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 devel- oped 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 understand- ing of how weather uncertainty propagates from the trajectory scale to the sector scale. All solutions proposed in this project will be evaluated and assessed using an advanced air traffic simulator. Introduction A better understanding of the elements introducing uncertainty in the traffic is key when optimizing, planning, executing, monitoring and synchronizing trajectories with ground systems and/or other aircraft. In particular, the need of computing ef- ficient, yet accurate trajectories becomes the fundamental cor- nerstone for reaching the expected benefits of TBO in terms of increased capacity, increased efficiency, and reduced environ- mental impact. The analysis of uncertainty in ATM must take into account the time horizon and the different scales of the system. While the spatial uncertainty affects mainly safety issues (loss of sep- aration) and efficiency, the temporal uncertainty manifests itself primarily as delay. Three scales of the system can be clearly differentiated: 1) microscale (a single flight); 2) mesoscale (air traffic); and 3) macroscale (the air transport network). Weather uncertainty is one of the main sources of uncertainty that affect the ATM system. In this project we focus on the anal- ysis of meteorological uncertainty coming from the following two sources: 1) wind, and 2) convective regions, including indi- vidual storm cells. Main Objectives The overall objective of the project is threefold: 1. To advance in the understanding of the effects of meteorolog- ical uncertainty in TBO. 2. To develop methodologies to quantify and reduce the effects of meteorological uncertainty in TBO. 3. To pave the road for a future integration of the management of meteorological uncertainty into the air traffic management system. The analysis of the effects of meteorological uncertainty in TBO is an extraordinarily broad task. In this project we focus on two particular problems, both at the pre-tactical and tactical levels: Trajectory planning and prediction of sector demand. These two problems correspond to two scales of the ATM system: air- craft trajectory (microscale) and air traffic in en-route sectors (mesoscale). Hence, in this project there are two particular ob- jectives: • To improve the predictability of aircraft trajectories when sub- ject to meteorological uncertainty keeping acceptable levels of efficiency, which is to be accomplished by developing a methodology to quantify the trade-off between predictability and efficiency. • To increase the accuracy of the prediction of sector demand when meteorological uncertainty is taken into account, which will be achieved by developing a methodology to measure the impact of improved trajectory planning under meteorological uncertainty on sector demand, that is, by quantifying the im- provement of the predictability of the sector demand when the predictability of the individual trajectories is improved. WP Structure Figure 1: Work-Package Structure Progress Results WP2 According to the requirements specified to the research topics of WP4 and WP5, the needed meteorological data sources, i.e., suitable ensemble prediction systems have been identified and the necessary processing methods defined. A spatial-temporal grid will be built: the processing covers coordinate transforma- tion from hybrid model levels to pressure levels, vertical interpo- lation, temporal downscaling and interpolation, spatial bilinear interpolation and the extraction of polygons which delimit ar- eas of e.g. deep convection. Further data processing is defined in order to calculate ensemble mean and spread of wind com- ponents and temperature which is used to quantify the forecast uncertainty of these meteorological parameters. While wind and temperature data is readily available as model output, informa- tion about convection can be derived from numerous parameters. Suitable indicators to describe convection have been identified. Figure 2: ECMWF-EPS and GLAM-EPS data and processing diagrams for wind, temperature, and convection. WP3 A TBO-Met Survey Questionnaire has been prepared. The fun- damental objectives of the survey are twofold: 1) ensure that TBO- Met project is aligned with current meteorological prac- tices in aviation (particularly any issue regarding meteorological uncertainty); and 2) understand future expectations and needs regarding meteorological uncertainty management. WP4 We present preliminary results on robust trajectory planning at pre-tactical level. The main goal is to predict trajectories that are efficient, yet predictable. State of the art Ensemble Probabilistic Forecasts are used as data for wind (assumed to be the unique source of uncertainty). An ad-hoc optimal control methodol- ogy has been developed to solve trajectory planning problems considering wind input from EPFs. A set of Pareto Optimal tra- jectories is obtained, in particular results for the minimum fuel trajectory and the most predictable trajectory are presented and discussed. Trade-off between fuel consumption and time disper- sion is obtained. It is shown how uncertainty can be quantified and reduced by proposing alternative trajectories. Please, refer to [1] for further information. 100 ◦ W 80 ◦ W 60 ◦ W 40 ◦ W 20 ◦ W 0 ◦ 30 ◦ N 40 ◦ N 50 ◦ N 60 ◦ N 60 ◦ N 0 . 0 1 . 5 3 . 0 4 . 5 6 . 0 7 . 5 9 . 0 10 . 5 p σ 2 u + σ 2 v p 29000 29500 30000 30500 31000 Fuel consumption (kg) 0.5 1.5 2.5 3.5 4.5 Arrival time range (minutes) p=0 p=50 p Figure 3: up: Optimal trajectories from NY to Lisbon, for values of p (pre- dictability parameter) from 0 (min. fuel) to 50 (max. predictability). Higher brightness in the trajectory color indicates higher values of p. We also color regions of higher uncertainty, which we have defined as p σ 2 u + σ 2 v , with σ u being the standard deviation of the u component of wind across different members and σ v analogous for the v-component. down: Pareto frontier of the problem 0 1000 2000 3000 4000 5000 6000 -150 -100 -50 0 50 100 150 200 Time lead or lag (s) 0 1000 2000 3000 4000 5000 6000 250 260 270 280 290 300 310 Ground speed (m/s) 0 1000 2000 3000 4000 5000 6000 Distance (km) 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Heading (rad) Course True heading 0 1000 2000 3000 4000 5000 6000 7000 -150 -100 -50 0 50 100 150 200 Time lead or lag (s) 0 1000 2000 3000 4000 5000 6000 7000 250 260 270 280 290 300 310 Ground speed (m/s) 0 1000 2000 3000 4000 5000 6000 7000 Distance (km) 0.5 1.0 1.5 2.0 2.5 3.0 Heading (rad) Course True heading Figure 4: State-space evolution in the case p =0 and p = 50 (notice that the solution to the problem provides a unique course, and a unique true airspeed [set as a constant parameter], both to be tracked. In addition, it provides different tracks and ground speeds associated to the different winds in the en- samble). Time leads or lags are defined with respect to the average trajectory. Left: min. fuel (p=0); Right: max. predictability (p=50) References [1] D. Gonz´ alez-Arribas, M. Soler, and M. Sanjurjo, “Wind- based robust trajectory optimization using meteorologi- cal ensemble probabilistic forecasts,” in SESAR Innovation Days, 2016. Acknowledgements TBO-MET project has received funding from the SESAR JU under grant agreement No 699294 under Euro- pean Union’s Horizon 2020 research and innovation pro- gramme. Consortium members are UNIVERSITY OF SEVILLE (Coordinator), AEMET (Agencia Espa˜ nola de Meteorolog´ ıa), METEOSOLUTIONS GmbH, and PARIS- LODRON-UNIVERSITAT SALZBURG, and UNIVERSITY CARLOS III DE MADRID. Website: https://tbomet-h2020.com
