Understanding Ice Crystal Habit Diversity with Self-Supervised Learning
This work addresses the challenge of ice crystal habit diversity for climate modeling, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of modeling ice-containing clouds by using self-supervised learning to learn latent representations of ice crystal shapes from imagery, demonstrating that these representations can quantify ice crystal diversity and improve climate characterization.
Ice-containing clouds strongly impact climate, but they are hard to model due to ice crystal habit (i.e., shape) diversity. We use self-supervised learning (SSL) to learn latent representations of crystals from ice crystal imagery. By pre-training a vision transformer with many cloud particle images, we learn robust representations of crystal morphology, which can be used for various science-driven tasks. Our key contributions include (1) validating that our SSL approach can be used to learn meaningful representations, and (2) presenting a relevant application where we quantify ice crystal diversity with these latent representations. Our results demonstrate the power of SSL-driven representations to improve the characterization of ice crystals and subsequently constrain their role in Earth's climate system.