https://doi.org/10.5194/wcd-2021-27 Preprint. Discussion started: 25 May 2021 c Author(s) 2021. CC BY 4.0 License. Automated detection and classification of synoptic scale fronts from atmospheric data grids Stefan Niebler1, Annette Miltenberger2, Bertil Schmidt1, and Peter Spichtinger2 1Institut für Informatik, Johannes Gutenberg Universität Mainz, Staudingerweg 7, 55128 Mainz, Germany 2Institut für Physik der Atmosphäre, Johannes Gutenberg Universität Mainz, Becherweg 21, 55128 Mainz, Germany Correspondence: Stefan Niebler (
[email protected]) Abstract. Automatic determination of fronts from atmospheric data is an important task for weather prediction. In this paper we introduce a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America. We apply label deformation within our loss function which removes the need for skeleton operations or other complicated post processing steps as observed in other 5 work, to create the final output. We observe good prediction scores with CSI higher than 62.9% and a Object Detection Rate of more than 73%. Frontal climatologies of our network are highly correlated (greater than 79.6%) to climatologies created from weather service data. Evaluated cross sections further show that our networks classification is physical plausible. Comparison with a well-established baseline method (ETH Zurich) shows a better performance of our network classification. Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 4.0 License. This li- 10 cence does not affect the Crown copyright work, which is re- usable under the Open Government Licence (OGL).