SwePub - search: WFRF:(Burauskaite Harju B.)

Enumeration	Reference	Cover	Find
1.	Burauskaite-Harju, Agne, et al. (author) Characterizing and visualizing spatio-temporal patterns in hourly precipitation records 2012 In: Journal of Theoretical and Applied Climatology. - : Springer. - 0177-798X .- 1434-4483. ; 109:3-4, s. 333-343 Journal article (peer-reviewed)abstract We develop new techniques to summarize and visualize 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 generalized to characterize 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 visualize such threeparameter 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.

Burauskaite-Harju, Agne, et al. (author)
Characterizing and visualizing spatio-temporal patterns in hourly precipitation records
2012
In: Journal of Theoretical and Applied Climatology. - : Springer. - 0177-798X .- 1434-4483. ; 109:3-4, s. 333-343
Journal article (peer-reviewed)abstract
- We develop new techniques to summarize and visualize 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 generalized to characterize 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 visualize such threeparameter 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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