  Air transportation is a complex system characterised by a plethora of
interactions at multiple temporal and spatial scales; as a consequence, even
simple dynamics like sequencing aircraft for landing can lead to the appearance
of emergent behaviours, which are both difficult to control and detrimental to
operational efficiency. We propose a model, based on a modulated Markov jitter,
to represent ordinal pattern properties of real landing operations in European
airports. The parameters of the model are tuned by minimising the distance
between the probability distributions of ordinal patterns generated by the real
and synthetic sequences, as estimated by the Permutation Jensen-Shannon
Distance. We show that the correlation between consecutive hours in the landing
flow changes between airports, and that it can be interpreted as a metric of
efficiency. We further compare the dynamics pre and post COVID-19, showing how
this has changed beyond what can be attributed to a simple reduction of
traffic. We finally draw some operational conclusions, and discuss the
applicability of these findings in a real operational environment.
