用自监督学习分析冰晶形态多样性，提升气候模型精度
Understanding Ice Crystal Habit Diversity with Self-Supervised Learning
- 用视觉变换器在大量云粒子图像上预训练，提取冰晶形态的潜在表征
- 通过潜空间量化冰晶形状差异，验证了方法的有效性
- 适合气候建模与大气科学领域研究者参考
含冰云对气候有重要影响，但因其冰晶形状多样难以准确建模。本文采用自监督学习（SSL）从冰晶图像中学习潜在表征。通过在大量云粒子图像上预训练视觉变换器，获得鲁棒的晶体形态表征，可用于多种科学任务。关键贡献包括：(1) 验证了该SSL方法可学习有意义的表征；(2) 展示了利用这些潜变量量化冰晶多样性的实际应用。结果表明，基于SSL的表征能有效提升冰晶特征刻画能力，进而约束其在地球气候系统中的作用。
原文摘要 · Abstract (English)
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.
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