Self-supervised Classification of Weather Systems Based on
Spatiotemporal Contrastive Learning
Classification of weather systems provides a simple description of
atmospheric circulations and bridges the gap between large-scale
atmospheric conditions and local-scale environmental variables. However,
the existing classification methods are challenged due to lack of labels
and inaccurate similarity measures between data samples. In this letter,
we propose a self-supervised Spatiotemporal Contrastive Learning (SCL)
framework for the classification of weather systems without manual
labels. In particular, we operate both spatial and temporal augmentation
on multivariate meteorological data to fully explore temporal context
information and spatial stability in accordance with synoptic nature.
With the classification results, we apply a statistical downscaling
method based on analog forecasting for the assessment and comparison of
classification results. The experimental results demonstrate that the
proposed SCL model outperforms traditional classification methods.