Abstract
The analysis of spatial patterns of co-variability of extreme rainfall is challenging because traditional techniques based on principal component analysis of the covariance matrix only capture the first two statistical moments of the data distribution and are thus not suitable to analyze the behavior in the tails of the respective distributions. Here, we describe an alternative to these techniques which is based on the combination of a nonlinear synchronization measure and complex network theory. This approach allows to derive spatial patterns encoding the co-variability of extreme rainfall at different locations. By introducing suitable network measures, the methodology can be used to perform climatological analysis but also for statistical prediction of extreme rainfall events. We introduce the methodological framework and present applications to high-spatiotemporal resolution rainfall data (TRMM 3B42) over South America.
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Acknowledgements
This work was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP, and the DFG project “Investigation of past and present climate dynamics and its stability by means of a spatio-temporal analysis of climate data using complex networks” (MA 4759/4-1).
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Boers, N., Rheinwalt, A., Bookhagen, B., Marwan, N., Kurths, J. (2015). A Complex Network Approach to Investigate the Spatiotemporal Co-variability of Extreme Rainfall. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_15
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DOI: https://doi.org/10.1007/978-3-319-17220-0_15
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