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

Related authors

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024,https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary

Related subject area

Dynamical processes in midlatitudes
From sea to sky: understanding the sea surface temperature impact on an atmospheric blocking event using sensitivity experiments with the ICOsahedral Nonhydrostatic (ICON) model
Svenja Christ, Marta Wenta, Christian M. Grams, and Annika Oertel
Weather Clim. Dynam., 6, 17–42, https://doi.org/10.5194/wcd-6-17-2025,https://doi.org/10.5194/wcd-6-17-2025, 2025
Short summary
Simulating record-shattering cold winters of the beginning of the 21st century in France
Camille Cadiou and Pascal Yiou
Weather Clim. Dynam., 6, 1–15, https://doi.org/10.5194/wcd-6-1-2025,https://doi.org/10.5194/wcd-6-1-2025, 2025
Short summary
Detection and consequences of atmospheric deserts: insights from a case study
Fiona Fix, Georg Mayr, Achim Zeileis, Isabell Stucke, and Reto Stauffer
Weather Clim. Dynam., 5, 1545–1560, https://doi.org/10.5194/wcd-5-1545-2024,https://doi.org/10.5194/wcd-5-1545-2024, 2024
Short summary
A global climatology of sting-jet extratropical cyclones
Suzanne L. Gray, Ambrogio Volonté, Oscar Martínez-Alvarado, and Ben J. Harvey
Weather Clim. Dynam., 5, 1523–1544, https://doi.org/10.5194/wcd-5-1523-2024,https://doi.org/10.5194/wcd-5-1523-2024, 2024
Short summary
The impact of preceding convection on the development of Medicane Ianos and the sensitivity to sea surface temperature
Claudio Sánchez, Suzanne Gray, Ambrogio Volonté, Florian Pantillon, Ségolène Berthou, and Silvio Davolio
Weather Clim. Dynam., 5, 1429–1455, https://doi.org/10.5194/wcd-5-1429-2024,https://doi.org/10.5194/wcd-5-1429-2024, 2024
Short summary

Cited articles

Acuna, D., Kar, A., and Fidler, S.: Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11075–11083, 2019. a
Berry, G., Reeder, M. J., and Jakob, C.: A global climatology of atmospheric fronts, Geophys. Res. Lett., 38, L04809, https://doi.org/10.1029/2010GL046451, 2011. a, b
Biard, J. C. and Kunkel, K. E.: Automated detection of weather fronts using a deep learning neural network, Adv. Stat. Clim. Meteorol. Oceanogr., 5, 147–160, https://doi.org/10.5194/ascmo-5-147-2019, 2019. a, b, c, d, e
Bitsa, E., Flocas, H., Kouroutzoglou, J., Hatzaki, M., Rudeva, I., and Simmonds, I.: Development of a Front Identification Scheme for Compiling a Cold Front Climatology of the Mediterranean, Climate, 7, 130, https://doi.org/10.3390/cli7110130, 2019. a
Bochenek, B., Ustrnul, Z., Wypych, A., and Kubacka, D.: Machine Learning-Based Front Detection in Central Europe, Atmosphere, 12, 1312, https://doi.org/10.3390/atmos12101312, 2021. a
Download
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.