Articles | Volume 6, issue 2
https://doi.org/10.5194/wcd-6-571-2025
https://doi.org/10.5194/wcd-6-571-2025
Research article
 | 
21 May 2025
Research article |  | 21 May 2025

Weather type reconstruction using machine learning approaches

Lucas Pfister, Lena Wilhelm, Yuri Brugnara, Noemi Imfeld, and Stefan Brönnimann

Related authors

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without a Summer of 1816
Peter Stucki, Lucas Pfister, Yuri Brugnara, Renate Varga, Chantal Hari, and Stefan Brönnimann
Clim. Past, 20, 2327–2348, https://doi.org/10.5194/cp-20-2327-2024,https://doi.org/10.5194/cp-20-2327-2024, 2024
Short summary
The weather of 1740, the coldest year in central Europe in 600 years
Stefan Brönnimann, Janusz Filipiak, Siyu Chen, and Lucas Pfister
Clim. Past, 20, 2219–2235, https://doi.org/10.5194/cp-20-2219-2024,https://doi.org/10.5194/cp-20-2219-2024, 2024
Short summary
A 258-year-long data set of temperature and precipitation fields for Switzerland since 1763
Noemi Imfeld, Lucas Pfister, Yuri Brugnara, and Stefan Brönnimann
Clim. Past, 19, 703–729, https://doi.org/10.5194/cp-19-703-2023,https://doi.org/10.5194/cp-19-703-2023, 2023
Short summary
Pre-industrial temperature variability on the Swiss Plateau derived from the instrumental daily series of Bern and Zurich
Yuri Brugnara, Chantal Hari, Lucas Pfister, Veronika Valler, and Stefan Brönnimann
Clim. Past, 18, 2357–2379, https://doi.org/10.5194/cp-18-2357-2022,https://doi.org/10.5194/cp-18-2357-2022, 2022
Short summary
Early instrumental meteorological observations in Switzerland: 1708–1873
Yuri Brugnara, Lucas Pfister, Leonie Villiger, Christian Rohr, Francesco Alessandro Isotta, and Stefan Brönnimann
Earth Syst. Sci. Data, 12, 1179–1190, https://doi.org/10.5194/essd-12-1179-2020,https://doi.org/10.5194/essd-12-1179-2020, 2020
Short summary

Related subject area

Dynamical processes in midlatitudes
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
Moisture transport axes: a unifying definition for tropical moisture exports, atmospheric rivers, and warm moist intrusions
Clemens Spensberger, Kjersti Konstali, and Thomas Spengler
Weather Clim. Dynam., 6, 431–446, https://doi.org/10.5194/wcd-6-431-2025,https://doi.org/10.5194/wcd-6-431-2025, 2025
Short summary
On the movement of atmospheric blocking systems and the associated temperature responses
Jonna van Mourik, Hylke de Vries, and Michiel Baatsen
Weather Clim. Dynam., 6, 413–429, https://doi.org/10.5194/wcd-6-413-2025,https://doi.org/10.5194/wcd-6-413-2025, 2025
Short summary
An ERA5 climatology of synoptic-scale negative potential vorticity–jet interactions over the western North Atlantic
Alexander Lojko, Andrew C. Winters, Annika Oertel, Christiane Jablonowski, and Ashley E. Payne
Weather Clim. Dynam., 6, 387–411, https://doi.org/10.5194/wcd-6-387-2025,https://doi.org/10.5194/wcd-6-387-2025, 2025
Short summary
Quantifying the spread in sudden stratospheric warming wave forcing in CMIP6
Verónica Martínez-Andradas, Alvaro de la Cámara, Pablo Zurita-Gotor, François Lott, and Federico Serva
Weather Clim. Dynam., 6, 329–343, https://doi.org/10.5194/wcd-6-329-2025,https://doi.org/10.5194/wcd-6-329-2025, 2025
Short summary

Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P. Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv [preprint], https://doi.org/10.48550/ARXIV.1603.04467, 16 March 2016a. a
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A system for large-scale machine learning, arXiv [preprint], https://doi.org/10.48550/ARXIV.1605.08695, 31 May 2016b. a
Accarino, G., Donno, D., Immorlano, F., Elia, D., and Aloisio, G.: An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis Data, Earth and Space Science, 10, e2023EA003106, https://doi.org/10.1029/2023EA003106, 2023. a
Barriendos, M., Martín-Vide, J., Peña, J. C., and Rodríguez, R.: Daily Meteorological Observations in Cádiz – San Fernando. Analysis of the Documentary Sources and the Instrumental Data Content (1786–1996), Climatic Change, 53, 151–170, https://doi.org/10.1023/A:1014991430122, 2002. a
Batista, G. E. A. P. A. and Monard, M. C.: A Study of K-Nearest Neighbour as an Imputation Method, in: Soft computing systems: design, management, and applications, edited by: Abraham, A., Köppen, M., and Ruiz-del Solar, J., IOS Press, Amsterdam, Frontiers in artificial intelligence and applications, 87, 251–260, ISBN 978-1-58603-297-5, 978-4-274-90558-2, 2002. a
Download
Short summary
Our work compares different machine learning approaches for creating long-term classifications of daily atmospheric circulation patterns using input data from surface meteorological observations. Our comparison reveals that a feedforward neural network performs best at this task. Using this model, we present a daily reconstruction of a commonly used weather type classification for central Europe that dates back to 1728.
Share