  Approach and landing accidents have resulted in a significant number of hull
losses worldwide. Technologies (e.g., instrument landing system) and procedures
(e.g., stabilized approach criteria) have been developed to reduce the risks.
In this paper, we propose a data-driven method to learn and interpret flight's
approach and landing parameters to facilitate comprehensible and actionable
insights into flight dynamics. Specifically, we develop two variants of tunnel
Gaussian process (TGP) models to elucidate aircraft's approach and landing
dynamics using advanced surface movement guidance and control system (A-SMGCS)
data, which then indicates the stability of flight. TGP hybridizes the
strengths of sparse variational Gaussian process and polar Gaussian process to
learn from a large amount of data in cylindrical coordinates. We examine TGP
qualitatively and quantitatively by synthesizing three complex trajectory
datasets and compared TGP against existing methods on trajectory learning.
Empirically, TGP demonstrates superior modeling performance. When applied to
operational A-SMGCS data, TGP provides the generative probabilistic description
of landing dynamics and interpretable tunnel views of approach and landing
parameters. These probabilistic tunnel models can facilitate the analysis of
procedure adherence and augment existing aircrew and air traffic controllers'
displays during the approach and landing procedures, enabling necessary
corrective actions.
