Abstract
A convolutional neural network was used to detect occurrences of pockets
of open cells (POCs). Trained on a small hand-logged dataset and applied
to 13 years of satellite imagery the neural network is able to classify
8,491 POCs. This extensive database allows the first robust analysis of
the spatial and temporal prevalence of these phenomena, as well as a
detailed analysis of their micro-physical properties. We find a large
(30%) increase in cloud effective radius inside POCs as compared to
their surroundings and similarly large (20%) decrease in cloud
fraction. This also allows their global radiative effect to be
determined. Using simple radiative approximations we find that the
instantaneous global mean top-of-atmosphere perturbation by all POCs is
only 0.01Wm-2.