  The observed atmospheric spectrum of exoplanets and brown dwarfs depends
critically on the presence and distribution of atmospheric condensates. The
Ackerman & Marley (2001) methodology for predicting the vertical distribution
of condensate particles is widely used to study cloudy atmospheres and has
recently been implemented in an open-source python package virga. The model
relies upon input parameter $f_{\text{sed}}$, the sedimentation efficiency,
which until now has been held constant. The relative simplicity of this model
renders it useful for retrieval studies due to its rapidly attainable
solutions. However, comparisons with more complex microphysical models such as
CARMA have highlighted inconsistencies between the two approaches, namely that
the cloud parameters needed for radiative transfer produced by virga are
dissimilar to those produced by CARMA. To address these discrepancies, we have
extended the original Ackerman and Marley methodology in virga to allow for
non-constant $f_{\text{sed}}$ values, in particular those that vary with
altitude. We discuss one such parameterization and compare the cloud mass
mixing ratio produced by virga with constant and variable $f_{\text{sed}}$
profiles to that produced by CARMA. We find that the variable $f_{\text{sed}}$
formulation better captures the profile produced by CARMA with heterogeneous
nucleation, yet performs comparatively to constant $f_{\text{sed}}$ for
homogeneous nucleation. In general, virga has the capacity to handle any
$f_{\text{sed}}$ with an explicit anti-derivative, permitting a plethora of
alternative cloud profiles that are otherwise unattainable by constant
$f_{\text{sed}}$ values. The ensuing flexibility has the potential to better
agree with increasingly complex models and observed data.
