Accurately identifying liquid water layers in mixed-phase clouds is
crucial for estimating cloud radiative effects. Lidar-based retrievals
are limited in optically thick or multilayer clouds, leading to positive
biases in simulated shortwave radiative fluxes. At the same time,
general circulation models also tend to overestimate the downwelling
shortwave radiation at the surface especially in the Southern Ocean
regions. To reduce this SW radiation bias in models, we first need
better observational-based retrievals for liquid detection which can
later be used for model validation. To address this, a
machine-learning-based liquid-layer detection method called VOODOO was
employed in a proof-of-concept study using a single column radiative
transfer model to compare shortwave cloud radiative effects of
liquid-containing clouds detected by Cloudnet and VOODOO to ground-based
and satellite observations. Results showed a reduction in shortwave
radiation bias, indicating that liquid-layer detection with
machine-learning retrievals can improve radiative transfer simulations.