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

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 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.