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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Acknowledgements
The authors are very grateful to the Swedish Meteorological and Hydrological Institute (SMHI) for providing the precipitation data, to Colin Jones at the Rossby Centre for valuable comments and discussions, and to the Swedish Research Council (VR), the Gothenburg Atmospheric Science Centre (GAC), and FORMAS (grant #2007-1048-8700∗51) for financial support to Deliang Chen and Alexander Walther.
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Burauskaite-Harju, A., Grimvall, A., Achberger, C. et al. Characterising and visualizing spatio-temporal patterns in hourly precipitation records. Theor Appl Climatol 109, 333–343 (2012). https://doi.org/10.1007/s00704-011-0574-x
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DOI: https://doi.org/10.1007/s00704-011-0574-x