{"ID":2873027,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.07688","arxiv_id":"2509.07688","title":"Understanding Ice Crystal Habit Diversity with Self-Supervised Learning","abstract":"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.","short_abstract":"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 re...","url_abs":"https://arxiv.org/abs/2509.07688","url_pdf":"https://arxiv.org/pdf/2509.07688v3","authors":"[\"Joseph Ko\",\"Hariprasath Govindarajan\",\"Fredrik Lindsten\",\"Vanessa Przybylo\",\"Kara Sulia\",\"Marcus van Lier-Walqui\",\"Kara Lamb\"]","published":"2025-09-09T12:54:20Z","proceeding":"physics.ao-ph","tasks":"[\"physics.ao-ph\",\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}