  When observing a phenomenon, severe cases or anomalies are often
characterised by deviation from the expected data distribution. However,
non-deviating data samples may also implicitly lead to severe outcomes. In the
case of unsupervised severe weather detection, these data samples can lead to
mispredictions, since the predictors of severe weather are often not directly
observed as features. We posit that incorporating external or auxiliary
information, such as the outcome of an external task or an observation, can
improve the decision boundaries of an unsupervised detection algorithm. In this
paper, we increase the effectiveness of a clustering method to detect cases of
severe weather by learning augmented and linearly separable latent
representations.We evaluate our solution against three individual cases of
severe weather, namely windstorms, floods and tornado outbreaks.
