Cloud Classification with Unsupervised Deep Learning
This work addresses cloud characterization for meteorology and climate science by enabling more detailed and data-driven classifications, though it appears incremental as it builds on existing neural network methods by shifting from supervised to unsupervised learning.
The paper tackled cloud classification by developing an unsupervised deep learning framework that learns directly from MODIS satellite radiance data, avoiding pre-defined categories and discovering novel classifications, with preliminary results showing it extracts physically relevant information and produces meaningful cloud classes.
We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies. While previous neural network-based cloud classification models have used supervised learning methods, unsupervised learning allows us to avoid restricting the model to artificial categories based on historical cloud classification schemes and enables the discovery of novel, more detailed classifications. Our framework learns cloud features directly from radiance data produced by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument, deriving cloud characteristics from millions of images without relying on pre-defined cloud types during the training process. We present preliminary results showing that our method extracts physically relevant information from radiance data and produces meaningful cloud classes.