Understanding Ice Crystal Habit Diversity with Self-Supervised Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915594440278016 |
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| author | Ko, Joseph Govindarajan, Hariprasath Lindsten, Fredrik Przybylo, Vanessa Sulia, Kara van Lier-Walqui, Marcus Lamb, Kara |
| author_facet | Ko, Joseph Govindarajan, Hariprasath Lindsten, Fredrik Przybylo, Vanessa Sulia, Kara van Lier-Walqui, Marcus Lamb, Kara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Ice Crystal Habit Diversity with Self-Supervised Learning Ko, Joseph Govindarajan, Hariprasath Lindsten, Fredrik Przybylo, Vanessa Sulia, Kara van Lier-Walqui, Marcus Lamb, Kara Atmospheric and Oceanic Physics Computer Vision and Pattern Recognition 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. |
| title | Understanding Ice Crystal Habit Diversity with Self-Supervised Learning |
| topic | Atmospheric and Oceanic Physics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.07688 |