  Classifying the state of the atmosphere into a finite number of large-scale
circulation regimes is a popular way of investigating teleconnections, the
predictability of severe weather events, and climate change. Here, we
investigate a supervised machine learning approach based on deformable
convolutional neural networks (deCNNs) and transfer learning to forecast the
North Atlantic-European weather regimes during extended boreal winter for 1 to
15 days into the future. We apply state-of-the-art interpretation techniques
from the machine learning literature to attribute particular regions of
interest or potential teleconnections relevant for any given weather cluster
prediction or regime transition. We demonstrate superior forecasting
performance relative to several classical meteorological benchmarks, as well as
logistic regression and random forests. Due to its wider field of view, we also
observe deCNN achieving considerably better performance than regular
convolutional neural networks at lead times beyond 5-6 days. Finally, we find
transfer learning to be of paramount importance, similar to previous
data-driven atmospheric forecasting studies.
