  Clouds play an important role in the Earth's energy budget and their behavior
is one of the largest uncertainties in future climate projections. Satellite
observations should help in understanding cloud responses, but decades and
petabytes of multispectral cloud imagery have to date received only limited
use. This study reduces the dimensionality of satellite cloud observations by
grouping them via a novel automated, unsupervised cloud classification
technique by using a convolutional neural network. Our technique combines a
rotation-invariant autoencoder with hierarchical agglomerative clustering to
generate cloud clusters that capture meaningful distinctions among cloud
textures, using only raw multispectral imagery as input. Thus, cloud classes
are defined without reliance on location, time/season, derived physical
properties, or pre-designated class definitions. We use this approach to
generate a unique new cloud dataset, the AI-driven cloud classification atlas
(AICCA), which clusters 22 years of ocean images from the Moderate Resolution
Imaging Spectroradiometer (MODIS) on NASA's Aqua and Terra instruments - 800 TB
of data or 198 million patches roughly 100 km x 100 km (128 x 128 pixels) -
into 42 AI-generated cloud classes. We show that AICCA classes involve
meaningful distinctions that employ spatial information and result in distinct
geographic distributions, capturing, for example, stratocumulus decks along the
West coasts of North and South America. AICCA delivers the information in
multi-spectral images in a compact form, enables data-driven diagnosis of
patterns of cloud organization, provides insight into cloud evolution on
timescales of hours to decades, and helps democratize climate research by
facilitating access to core data.
