  Advanced satellite-born remote sensing instruments produce high-resolution
multi-spectral data for much of the globe at a daily cadence. These datasets
open up the possibility of improved understanding of cloud dynamics and
feedback, which remain the biggest source of uncertainty in global climate
model projections. As a step towards answering these questions, we describe an
automated rotation-invariant cloud clustering (RICC) method that leverages deep
learning autoencoder technology to organize cloud imagery within large datasets
in an unsupervised fashion, free from assumptions about predefined classes. We
describe both the design and implementation of this method and its evaluation,
which uses a sequence of testing protocols to determine whether the resulting
clusters: (1) are physically reasonable, (i.e., embody scientifically relevant
distinctions); (2) capture information on spatial distributions, such as
textures; (3) are cohesive and separable in latent space; and (4) are
rotationally invariant, (i.e., insensitive to the orientation of an image).
Results obtained when these evaluation protocols are applied to RICC outputs
suggest that the resultant novel cloud clusters capture meaningful aspects of
cloud physics, are appropriately spatially coherent, and are invariant to
orientations of input images. Our results support the possibility of using an
unsupervised data-driven approach for automated clustering and pattern
discovery in cloud imagery.
