Articles | Volume 3, issue 1
https://doi.org/10.5194/wcd-3-113-2022
https://doi.org/10.5194/wcd-3-113-2022
Research article
 | 
01 Feb 2022
Research article |  | 01 Feb 2022

Automated detection and classification of synoptic-scale fronts from atmospheric data grids

Stefan Niebler, Annette Miltenberger, Bertil Schmidt, and Peter Spichtinger

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Short summary
We use machine learning to create a network that detects and classifies four types of synoptic-scale weather fronts from ERA5 atmospheric reanalysis data. We present an application of our method, showing its use case in a scientific context. Additionally, our results show that multiple sources of training data are necessary to perform well on different regions, implying differences within those regions. Qualitative evaluation shows that the results are physically plausible.