Articles | Volume 2, issue 3
Research article
12 Jul 2021
Research article |  | 12 Jul 2021

An unsupervised learning approach to identifying blocking events: the case of European summer

Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack

Data sets

Ground Truth Dataset for European summer blocking events 1979-2019 Carl Thomas

Model code and software

SOM-BI Scripts Carl Thomas, Gerald Lim, and Peer Nowack

Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.