  The construction and implementation of atmospheric model grids is a popular
tool in exoplanet characterisation. These typically vary a number of parameters
linearly, containing one model for every combination of parameter values. Here
we investigate alternative methods of sampling parameters, including random
sampling and Latin hypercube (LH) sampling, and how these compare to linearly
sampled grids. We use a random forest to analyse the performance of these grids
for two different models, as well as investigate the information content of the
particular model grid from Goyal et al. 2019. We also use nested-sampling to
implement mock atmospheric retrievals on simulated JWST transmission spectra by
interpolating on linearly sampled model grids. Our results show that random or
LH sampling out-performs linear sampling in parameter predictability for our
higher dimensional models, requiring fewer models in the grid, and thus
allowing for more computationally intensive forward models to be used. We also
find that using a traditional retrieval with interpolation on a linear grid can
produce biased posterior distributions, especially for parameters with
non-linear effects on the spectrum. In particular, we advise caution when
performing linear interpolation on the C/O ratio, cloud properties, and
metallicity. Finally, we find that the information content analysis of the grid
from Goyal et al. 2019 is able to highlight key areas of the spectra where the
presence or absence of certain molecules can be detected, providing good
indicators for parameters such as temperature and C/O ratio.
