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  Non-linear time series analysis of precipitation events using regional climate networks for Germany

Rheinwalt, A., Boers, N., Marwan, N., Kurths, J., Hoffmann, P., Gerstengarbe, F.-W., Werner, P. C. (2016): Non-linear time series analysis of precipitation events using regional climate networks for Germany. - Climate Dynamics, 46, 3, 1065-1074.
https://doi.org/10.1007/s00382-015-2632-z

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 Creators:
Rheinwalt, Aljoscha1, Author              
Boers, Niklas1, Author              
Marwan, Norbert1, Author              
Kurths, Jürgen1, Author              
Hoffmann, Peter1, Author              
Gerstengarbe, Friedrich-Wilhelm1, Author              
Werner, Peter C.1, Author              
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Synchronous occurrences of heavy rainfall events and the study of their relation in time and space are of large socio-economical relevance, for instance for the agricultural and insurance sectors, but also for the general well-being of the population. In this study, the spatial synchronization structure is analyzed as a regional climate network constructed from precipitation event series. The similarity between event series is determined by the number of synchronous occurrences. We propose a novel standardization of this number that results in synchronization scores which are not biased by the number of events in the respective time series. Additionally, we introduce a new version of the network measure directionality that measures the spatial directionality of weighted links by also taking account of the effects of the spatial embedding of the network. This measure provides an estimate of heavy precipitation isochrones by pointing out directions along which rainfall events synchronize. We propose a climatological interpretation of this measure in terms of propagating fronts or event traces and confirm it for Germany by comparing our results to known atmospheric circulation patterns.

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 Dates: 2016
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s00382-015-2632-z
PIKDOMAIN: Climate Impacts & Vulnerabilities - Research Domain II
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 6995
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Extremes
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Regional keyword: Germany
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Working Group: Hydroclimatic Risks
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Climate Dynamics
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 46 (3) Sequence Number: - Start / End Page: 1065 - 1074 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals77