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

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

doi:https://doi.org/10.5194/wcd-3-113-2022

Articles | Volume 3, issue 1

https://doi.org/10.5194/wcd-3-113-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wcd-3-113-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 3, issue 1

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

Supplement

https://doi.org/10.5194/wcd-3-113-2022-supplement

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

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.