Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data

Andreas Holm Nielsen, Alexandros Iosifidis, Henrik Karstoft

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


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.
Original languageEnglish
Article number8395
JournalScientific Reports
Number of pages12
Publication statusPublished - May 2022


  • Atmosphere
  • Forecasting
  • Machine Learning
  • Neural Networks, Computer
  • Weather

Cite this