Speaker
Description
The representation of shallow trade-wind convective clouds in climate models
dominates the uncertainty in climate sensitivity estimates. In
particular the
radiative impact of cloud spatial organisation is poorly understood.
This work
presents the first unsupervised neural network model which
autonomously discovers cloud organisation regimes in satellite images.
Trained
on 10,000 GOES-16 satellite images (tropical Atlantic, boreal winter) the
regimes found are shown to exist in a hierarchy of organisational
scales, with
sub-clusters having distinct radiative properties. The model requires no
time-consuming and subjective hand-labelled data based on predefined
structures
allowing for objective study of very large datasets. The model enables
study of
environmental conditions in different organisational regimes and transitions
between regimes, and objective comparisons of model behaviour with
observations
through cloud structures emerging in both. These abilities enable
discovery of
previously unknown physical relationships in cloud processes, enabling
better
representation of clouds in weather and climate simulations.
