Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data
This work addresses weather forecasting for meteorologists and climate scientists, offering incremental improvements in prediction accuracy for large-scale circulation patterns.
The paper tackled the problem of forecasting North Atlantic-European weather regimes up to 15 days ahead using deformable convolutional neural networks (deCNNs) and transfer learning, achieving superior performance over classical meteorological benchmarks and other machine learning methods, with deCNNs outperforming regular CNNs beyond 5-6 days due to a wider field of view.
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.