Cloud Clustering Over January 2003 via Scalable Rotationally Invariant Autoencoder.

Unsupervised fashion of cloud analysis has the significant possibility of exploring massive quantities of satellite cloud imagery to discover unknown cloud patterns that can be relevant to climate change research, free from the assumption of artificial cloud categories. We describe a further development of rotation-invariant cloud clustering (RICC) that leverages unsupervised deep learning autoencoder and clustering to be scaled for larger cloud datasets. Results suggest that our rotation-invariant autoencoder shows high scalability conditioned on the size of GPUs, and the clusters generated from RICC on the month-long dataset capture unique spatial patterns with distinct cloud physical properties.