Characterising and visualizing spatio-temporal patterns in hourly precipitation records |
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| Authors: | Agne Burauskaite-Harju Anders Grimvall Christine Achberger Alexander Walther Deliang Chen |
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| Institution: | 1. Division of Statistics, Department of Computer and Information Science, Link?ping University, 58183, Link?ping, Sweden
2. Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
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| Abstract: | We develop new techniques to summarise and visualise spatial patterns of coincidence in weather events such as more or less heavy precipitation at a network of meteorological stations. The cosine similarity measure, which has a simple probabilistic interpretation for vectors of binary data, is generalised to characterise spatial dependencies of events that may reach different stations with a variable time lag. More specifically, we reduce such patterns into three parameters (dominant time lag, maximum cross-similarity, and window-maximum similarity) that can easily be computed for each pair of stations in a network. Furthermore, we visualise such three-parameter summaries by using colour-coded maps of dependencies to a given reference station and distance-decay plots for the entire network. Applications to hourly precipitation data from a network of 93 stations in Sweden illustrate how this method can be used to explore spatial patterns in the temporal synchrony of precipitation events. |
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