Weather type reconstruction using machine learning approaches
<p>Weather types are used to characterise large-scale synoptic weather patterns over a region. Long-standing records of weather types hold important information about day-to-day variability and changes in atmospheric circulation and the associated effects on the surface. However, most weather...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-05-01
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| Series: | Weather and Climate Dynamics |
| Online Access: | https://wcd.copernicus.org/articles/6/571/2025/wcd-6-571-2025.pdf |
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| author | L. Pfister L. Pfister L. Wilhelm L. Wilhelm Y. Brugnara Y. Brugnara Y. Brugnara N. Imfeld N. Imfeld S. Brönnimann S. Brönnimann |
| author_facet | L. Pfister L. Pfister L. Wilhelm L. Wilhelm Y. Brugnara Y. Brugnara Y. Brugnara N. Imfeld N. Imfeld S. Brönnimann S. Brönnimann |
| author_sort | L. Pfister |
| collection | DOAJ |
| description | <p>Weather types are used to characterise large-scale synoptic weather patterns over a region. Long-standing records of weather types hold important information about day-to-day variability and changes in atmospheric circulation and the associated effects on the surface. However, most weather type reconstructions are restricted in their temporal extent and suffer from methodological limitations. In our study, we assess various machine learning approaches for station-based weather type reconstruction over Europe based on the nine-class cluster analysis of principal components (CAP9) weather type classification. With a common feedforward neural network performing best in this model comparison, we reconstruct a daily CAP9 weather type series back to 1728. This new reconstruction constitutes the longest daily weather type series available. Detailed validation shows considerably better performance compared to previous statistical approaches and good agreement with the reference series for various climatological analyses. Our approach may serve as a guide for other weather type classifications.</p> |
| format | Article |
| id | doaj-art-da58b2ac6b8c45b48bde74bb95b99ad3 |
| institution | OA Journals |
| issn | 2698-4016 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Weather and Climate Dynamics |
| spelling | doaj-art-da58b2ac6b8c45b48bde74bb95b99ad32025-08-20T01:52:38ZengCopernicus PublicationsWeather and Climate Dynamics2698-40162025-05-01657159410.5194/wcd-6-571-2025Weather type reconstruction using machine learning approachesL. Pfister0L. Pfister1L. Wilhelm2L. Wilhelm3Y. Brugnara4Y. Brugnara5Y. Brugnara6N. Imfeld7N. Imfeld8S. Brönnimann9S. Brönnimann10Oeschger Centre for Climate Change Research, University of Bern, Bern 3012, SwitzerlandInstitute of Geography, University of Bern, Bern 3012, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern 3012, SwitzerlandInstitute of Geography, University of Bern, Bern 3012, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern 3012, SwitzerlandInstitute of Geography, University of Bern, Bern 3012, Switzerlandnow at: Empa, Dübendorf 8600, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern 3012, SwitzerlandInstitute of Geography, University of Bern, Bern 3012, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern 3012, SwitzerlandInstitute of Geography, University of Bern, Bern 3012, Switzerland<p>Weather types are used to characterise large-scale synoptic weather patterns over a region. Long-standing records of weather types hold important information about day-to-day variability and changes in atmospheric circulation and the associated effects on the surface. However, most weather type reconstructions are restricted in their temporal extent and suffer from methodological limitations. In our study, we assess various machine learning approaches for station-based weather type reconstruction over Europe based on the nine-class cluster analysis of principal components (CAP9) weather type classification. With a common feedforward neural network performing best in this model comparison, we reconstruct a daily CAP9 weather type series back to 1728. This new reconstruction constitutes the longest daily weather type series available. Detailed validation shows considerably better performance compared to previous statistical approaches and good agreement with the reference series for various climatological analyses. Our approach may serve as a guide for other weather type classifications.</p>https://wcd.copernicus.org/articles/6/571/2025/wcd-6-571-2025.pdf |
| spellingShingle | L. Pfister L. Pfister L. Wilhelm L. Wilhelm Y. Brugnara Y. Brugnara Y. Brugnara N. Imfeld N. Imfeld S. Brönnimann S. Brönnimann Weather type reconstruction using machine learning approaches Weather and Climate Dynamics |
| title | Weather type reconstruction using machine learning approaches |
| title_full | Weather type reconstruction using machine learning approaches |
| title_fullStr | Weather type reconstruction using machine learning approaches |
| title_full_unstemmed | Weather type reconstruction using machine learning approaches |
| title_short | Weather type reconstruction using machine learning approaches |
| title_sort | weather type reconstruction using machine learning approaches |
| url | https://wcd.copernicus.org/articles/6/571/2025/wcd-6-571-2025.pdf |
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