The Mediterranean region frequently experiences extreme precipitation events (EPEs) with devastating consequences for affected societies, economies, and environment. Thus, it is crucial to better understand their characteristics and drivers and improve their predictions at longer lead times. This work provides new insights about the spatiotemporal dependencies of EPEs in the region. It, moreover, implements Empirical Orthogonal Function analysis and subsequent non-hierarchical Kmeans clustering for generating nine distinct weather patterns over the domain, referred to as “Mediterranean patterns”. These patterns are significantly associated with EPEs across the region, and in fact, can be used to extend the forecasting horizon of EPEs. This is demonstrated considering modelled data for all the domain, but also using observational data for Calabria, southern Italy, an area of complex topography that increases the challenges of weather prediction. The results suggest preferential techniques for improving EPEs predictions for short, medium, and extended range forecasts, supporting thus the mitigation of their negative impacts.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:85612 |
| Date | 31 May 2023 |
| Creators | Mastrantonas, Nikolaos |
| Contributors | Matschullat, Jörg, Pappenberger, Florian, Magnusson, Linus, Raveh-Rubin, Shira, Technische Universität Bergakademie Freiberg |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1002/joc.6985, 10.1002/qj.4236, 10.1002/met.2101 |
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