  The representation of clouds and associated processes of rain and snow
formation remains one of the major uncertainties in climate and weather
prediction models. In a companion paper (Part I), we systematically derived a
two moment bulk cloud microphysics model for collision and coalescence in warm
rain based on the kinetic coalescence equation (KCE) and used stochastic
approximations to close the higher order moment terms, and do so independently
of the collision kernel. Conservation of mass and consistency of droplet number
concentration of the evolving cloud properties were combined with numerical
simulations to reduce the parametrization problem to three key parameters.
  Here, we constrain these three parameters based on the physics of collision
and coalescence resulting in a "region of validity." Furthermore, we
theoretically validate the new bulk model by deriving a subset of the "region
of validity" that contains stochastic parameters that skillfully reproduces an
existing model based on an a priori droplet size distribution by Seifert and
Beheng (2001). The stochastic bulk model is empirically validated against this
model, and parameter values that faithfully reproduce detailed KCE results are
identified.
  Furthermore, sensitivity tests indicate that the stochastically derived model
can be used with a time step as large as 30 seconds without significantly
compromising accuracy, which makes it very attractive to use in medium to long
range weather prediction models. These results can be explored in the future to
select parameters in the "region of validity" that are conditional on
environmental conditions and the age of the cloud.
