Articles | Volume 3, issue 1
Weather Clim. Dynam., 3, 113–137, 2022
https://doi.org/10.5194/wcd-3-113-2022
Weather Clim. Dynam., 3, 113–137, 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 et al.

Data sets

Front polylines extracted from DWD Maps S. Niebler https://doi.org/10.5281/zenodo.5785816

National Weather Service Coded Surface Bulletins, 2003- National Weather Service https://doi.org/10.5281/zenodo.2642801

Model code and software

FrontDetection S. Niebler https://doi.org/10.5281/zenodo.5783934

Video supplement

Detected Fronts January 2016 S. Niebler https://doi.org/10.5446/54716

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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.