becomes a corridor of uncertainty. First, we understand that the cone grows with the flight envelope, but afterwards, the uncertainty is bounded by the fact that the trajectories, as functions of time, are strictly increasing because of the filtering conditions of the dataset (cf. Section \[subsec:dataset\]) and the upper bound that is the cruise level.

![Trajectory Fitting[]{data-label="fig:traj"}](./img/traj.pdf){width="2.5in"}

![Rate of Climb Modeling[]{data-label="fig:roc"}](./img/roc.pdf){width="2.5in"}

Online Trajectory Predictor {#sec:Online}
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In this section, we present an online trajectory prediction which uses the observed positions of a current flight to tune at the same time the parameters of the model used to predict the rest of the trajectory. Different kind of algorithms can be used to undertake this task. Traditionally, a probabilistic approach is used in this online configuration, e.g. Kalman Filter, where the uncertainty is explicitly modeled. These performs very well on short periods of time but, for longer period like in this context, the linearity of the model could be too limited and should be subject to future experiments. In our approach, we use the BADA model in conjunction with the optimization algorithm CMA-ES in order to fit the parameters of the model with an objective function that defines a distance between the 