Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder

TL;DR

This paper introduces an unsupervised, AI-driven cloud classification method using a rotationally invariant autoencoder to analyze 22 years of satellite data, revealing new cloud patterns and climate-related changes.

Contribution

It presents a novel AI-based cloud classification atlas (AICCA) utilizing unsupervised learning and rotation-invariant clustering on large satellite datasets.

Findings

Identified 42 distinct cloud classes at ~100 km resolution.

Detected significant trends in cloud class changes over time.

Demonstrated the method's ability to uncover climate-relevant cloud patterns.

Abstract

Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections. However, the use of satellite observations to understand cloud feedbacks in a warming climate has been hampered by the simplicity of existing cloud classification schemes, which are based on single-pixel cloud properties rather than utilizing spatial structures and textures. Recent advances in computer vision enable the grouping of different patterns of images without using human-predefined labels, providing a novel means of automated cloud classification. This unsupervised learning approach allows discovery of unknown climate-relevant cloud patterns, and the automated processing of large datasets. We describe here the use of such methods to generate a new AI-driven Cloud Classification Atlas (AICCA), which leverages 22 years and 800 terabytes of MODIS satellite observations over the global ocean. We use a rotation-invariant cloud clustering (RICC) method to classify those observations into 42 AI-generated cloud class labels at ~100 km spatial resolution. As a case study, we use AICCA to examine a recent finding of decreasing cloudiness in a critical part of the subtropical stratocumulus deck, and show that the change is accompanied by strong trends in cloud classes.