This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there limit much improvements gained using classical strategies. Generative adversarial networks (GAN) have been demonstrated generate impressive ...