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
Environments and lifting mechanisms of cold-frontal convective cells during the warm season in Germany
George Pacey, Stephan Pfahl, and Lisa Schielicke
Weather Clim. Dynam., 6, 695–713, https://doi.org/10.5194/wcd-6-695-2025,https://doi.org/10.5194/wcd-6-695-2025, 2025
Short summary
Seasonal to decadal variability and persistence properties of the Euro-Atlantic jet streams characterized by complementary approaches
Hugo Banderier, Alexandre Tuel, Tim Woollings, and Olivia Martius
Weather Clim. Dynam., 6, 715–739, https://doi.org/10.5194/wcd-6-715-2025,https://doi.org/10.5194/wcd-6-715-2025, 2025
Short summary
Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology
Yvonne Anderson, Jacob Perez, and Amanda C. Maycock
Weather Clim. Dynam., 6, 595–608, https://doi.org/10.5194/wcd-6-595-2025,https://doi.org/10.5194/wcd-6-595-2025, 2025
Short summary
Weather type reconstruction using machine learning approaches
Lucas Pfister, Lena Wilhelm, Yuri Brugnara, Noemi Imfeld, and Stefan Brönnimann
Weather Clim. Dynam., 6, 571–594, https://doi.org/10.5194/wcd-6-571-2025,https://doi.org/10.5194/wcd-6-571-2025, 2025
Short summary
Temporally and zonally varying atmospheric waveguides – climatologies and connections to quasi-stationary waves
Rachel H. White and Lualawi Mareshet Admasu
Weather Clim. Dynam., 6, 549–570, https://doi.org/10.5194/wcd-6-549-2025,https://doi.org/10.5194/wcd-6-549-2025, 2025
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.
Share