Evaluation of nine gridded daily weather reconstructions for the European heatwave summer of 1807

Stucki, Peter; Brönnimann, Stefan; Imfeld, Noemi; Pfister, Lucas; Ruth, Conall E.; Schmutz, Yannis V.; Brugnara, Yuri; Wegmann, Martin; Przybylak, Rajmund; Filipiak, Janusz

doi:10.5194/cp-22-517-2026

Articles | Volume 22, issue 3

https://doi.org/10.5194/cp-22-517-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/cp-22-517-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 3

Research article

| Highlight paper

|

05 Mar 2026

Research article | Highlight paper |

| 05 Mar 2026

Evaluation of nine gridded daily weather reconstructions for the European heatwave summer of 1807

Peter Stucki, Stefan Brönnimann, Noemi Imfeld, Lucas Pfister, Conall E. Ruth, Yannis V. Schmutz, Yuri Brugnara, Martin Wegmann, Rajmund Przybylak, and Janusz Filipiak

Supplement

https://doi.org/10.5194/cp-22-517-2026-supplement

Data sets

Datasets for research article "Evaluation of nine gridded daily weather reconstructions for the European heatwave summer of 1807" P. Stucki et al. https://doi.org/10.48620/91594

The International Surface Pressure Databank version 4 G. P. Compo et al. https://doi.org/10.5065/9EYR-TY90

Swiss Early Meteorological Observations v2.0 Y. Brugnara https://doi.org/10.1594/PANGAEA.948258

Early instrumental air pressure and temperature measurements for Rovereto and Bolzano/Bozen (1800-1873) Y. Brugnara et al. https://doi.org/10.1594/PANGAEA.961277

Editorial statement

This study uses historical weather observations rescued from across Europe, as well as machine learning techniques to improve our understanding of a series of heatwaves in 1806. The results indicate that while machine learning models can do a good job of reproducing historical weather observations, they can't capture the physics behind why a heatwave would occur.

Short summary

We test nine reconstructions of Europe’s hot summer of 1807, using weather records, reanalyses, machine-learning (ML), and data assimilation. Most approaches match observed temperature and pressure well. Approaches based on physics of atmospheric flow capture weather patterns well, while ML approaches better reflect station records. Ingestion of accurate records from new regions improves the reconstructions markedly. In all, the approaches provide new insights to pre-industrial extreme weather.