  Considering the relatively high precision that will be reached by future
observatories, it has recently become clear that one dimensional (1D)
atmospheric models, in which the atmospheric temperature and composition of a
planet are considered to vary only in the vertical, will be unable to represent
exoplanetary transmission spectra with a sufficient accuracy. This is
particularly true for warm to (ultra-) hot exoplanets because the atmosphere is
unable to redistribute all the energy deposited on the dayside, creating a
strong thermal and often compositional dichotomy on the planet. This situation
is exacerbated by transmission spectroscopy, which probes the terminator
region. This is the most heterogeneous region of the atmosphere. However, if
being able to compute transmission spectra from 3D atmospheric structures (from
a global climate model, e.g.) is necessary to predict realistic observables, it
is too computationally expensive to be used in a data inversion framework. For
this reason, there is a need for a medium-complexity 2D approach that captures
the most salient features of the 3D model in a sufficiently fast
implementation. With this in mind, we present a new open-source documented
version of Pytmosph3R that handles the computation of transmission spectra for
atmospheres with up to three spatial dimensions and can account for time
variability. Taking the example of an ultra hot Jupiter, we illustrate how the
changing orientation of the planet during the transit can allow us to probe the
horizontal variations in the atmosphere. We further implement our algorithm in
TauREx to allow the community to perform 2D retrievals. We describe our
extensive cross-validation benchmarks and discuss the accuracy and numerical
performance of each model.
